Embodiments of the disclosure relate to the field of cybersecurity, and more specifically, to techniques that detect delayed activation malware by manipulating time seen by the malware using a controller operating within a computer runtime environment.
Over the last decade, malicious software (malware) has become a pervasive problem. In some situations, malware is an object embedded within downloadable content designed to adversely influence or attack normal operations of a computer. Examples of different types of malware may include computer viruses, Trojan horses, worms, spyware, backdoors, and other programming that operates maliciously within an electronic device (e.g. computer, tablet, smartphone, server, router, wearable technology, or other types of electronics with data processing capability) without permission by the user or an administrator.
For instance, content may be embedded with objects associated with a web page hosted by a malicious web site. By downloading this content, malware causing another web page to be requested from a malicious web site may be unknowingly installed on the computer. Similarly, malware may also be installed on a computer upon receipt or opening of an electronic mail (email) message. For example, an email message may contain an attachment, such as a Portable Document Format (PDF) document, with embedded executable malware. Also, malware may exist in files infected through any of a variety of attack vectors, which are uploaded from an infected computer onto a networked storage device such as a file share.
Over the past few years, various types of security appliances have been deployed at different segments of a network. These security appliances use virtual machines to uncover the presence of malware embedded within ingress content propagating over these different segments. However, in order to be effective in uncovering the malware, the virtual machine (VM) needs to make the suspicious content believe that it has accessed a live machine and not just a VM or “sandbox” environment. Thus, any actions that are performed in the VM or “sandbox” have to be as invisible as possible to malware so that the malware does not detect that it is being processed within a sandbox rather than a production computer system. Further, the VM may only exercise each specimen for a finite amount of time so that it may subsequently analyze as many specimens as possible in as short a period of time as possible. Accordingly, some malicious contents employ special defensive strategies to enable them to delay their own execution in order to bypass the VM uncovering their presence.
Embodiments of the invention are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings, in which like references indicate similar elements and in which:
Various embodiments of the disclosure relate to a malicious content detection (MCD) system and a corresponding method for manipulating time using a virtual machine and at least one time controller operating within the MCD system in order to capture and respond to an action (such as a time check) originating from malware that is attempting to delay its own execution. In other embodiments, a primary controller is employed to configure and manage the time controllers pursuant to an action plan.
In some embodiments, to uncover the malware effectively, the malware must first be convinced that it is running on a “live” machine and not in a VM environment that is implemented within the MCD system. For instance, since many malware are able to delay their own execution to bypass the VM's detection, in some embodiments, the MCD system may capture the delayed behavior of the malware by manipulating the time being obtained by the malware. For instance, the malware may delay its own execution in the VM by: (i) checking the current time using a software call (or other time check) to delay completion of its execution (e.g., sleep for 30 minutes), (ii) repeatedly checking whether the time has passed using one or more other software calls (or other time checks), and (iii) executing once it has determined that the delay has passed. To capture this delayed behavior, the MCD system may manipulate the time that is being obtained by the malware to convince it that the delay has passed such that the malware may execute when, in reality, only a short finite amount of time has passed (e.g., less than the delay for which the malware is waiting). Accordingly, embodiments of the invention are operable in order to effectively convince malware that (a) the malware is not in a VM environment, and (b) a manipulated time that is much longer than the real time has elapsed.
More specifically, a content specimen is processed over a short period of time (real time) in the runtime environment but, through the disclosed mechanisms, is led to believe it is processed for a much longer time. This is intended to trick the specimen into activation in the event it constitutes a delayed activation malware (time bomb) so it may be detected and classified as malware by the MCD. More specifically, in some embodiments of the invention, a time controller detects requests from or other time checks by the specimen (or from the process running the specimen) for the current time and provides a “false” time or clock as a response to achieve time dilation.
In various embodiments, the time controller includes one or more modules, logic or agents (which terms may be used interchangeably), each situated, for example, as a user-mode process, as a kernel-mode process, as a process running in or with a hypervisor, functionality embedded in virtual chipsets, and/or in the host operating system (“OS”). These various locations may be referred to as layers in the functional stack of the run-time environment. The time controller includes (a) a monitoring agent located in each of one or more of the layers configured to intercept, trap or, in common jargon, “hook” a software call (e.g., API, library, procedure, function, or system call) or other time check that seeks to check “current time,” and (b) time-dilation logic also located in one or more of the layers, each preferably different and “lower” in the stack than the monitoring agent, and configured to respond to the software call or other time check by providing a “false” current time that indicates considerably more time has transpired than the real clock. As an alternative to (or in addition to) the just-mentioned software call-response technique, the time control logic can be equipped to utilize one or more other techniques to establish the “false” time, including periodic or aperiodic polling, and in-memory or “op code” based techniques.
The response to the software call or other time check intercepted by the monitor agent may be made by the time-dilation logic explicitly via the monitoring agent or directly to the requester (e.g., software component, such as a process or operating system) within the software stack, or implicitly by modification of clock information (e.g., in memory) targeted by the software call or other time check, as appropriate and, preferably, as would be expected by malware if the call or time check so originated from malware.
In some embodiments, each time controller may have a set of two or more monitoring agents that share a single time-dilation logic, where the monitoring logic modules may be located in the same layer in the software stack or in different layers. The time-dilation logic can provide the same time dilation clock to all the monitoring modules in the set or provide different time-dilation clocks to each. Moreover, the time-dilation logic may provide time-dilation clocks to each monitoring agents in a fashion that is consistent from agent to agent with respect to establishing the false clock, particularly in so far as the software calls or other techniques used by malware to check time may arrive at somewhat different times and the false time provided in response must account for this difference in order to trick the malware. Moreover the malware may use more than one request (or one or more than one technique) in order to be assured that time manipulation within a malware detector is not taking place.
Various embodiments may employ a virtual run-time environment established, for example, by a hypervisor instantiating one or more virtual machines for processing a specimen. Each virtual machine may execute a guest image including a guest operating system (OS) and an application instance (process). Each monitoring agent and time-dilation logic of a time controller may reside inside one of the following: (i) the guest image (e.g., the specimen or process running in the user space or kernel space of the guest image), (ii) the host OS software, (iii) the hypervisor, or (iv) a virtualized device. Each of these components may be implemented as an independent process in the guest image or on or beneath the host OS. The monitoring agent may be located within any of these, and the time-dilation logic may be co-located (same layer) or may be located, at least in part, in another of these layers, e.g., the monitoring agent may be located closer to the specimen being processed. For example, the monitoring agent may be located within a process (or as a process) of the guest image and the time-dilation logic may be in communication with the monitoring agent and operating within or with the hypervisor. Accordingly, depending on the location of the time controller(s), kernel mode or user mode activities of the guest image may each see a different clock/time or they may all see the same clock/time. Each time controller can be referred to as an “elastic” time controller since its time-dilation logic may be configured to establish “short” or “long” delays. In other words, the time-dilation logic of a time controller can respond to a software call or other time check from a specimen being processed with a response containing a selected “false” time that induces the specimen into believing it has been dormant or inactive for a shorter period of time or a longer period of time depending on detection needs.
The time-dilation logic can decide on the length of delay based on prior results, e.g., static analysis results for the specimen, metadata regarding the specimen, identity of processes running in the run-time environment, experiential knowledge in dealing with known malware, current analysis parameters such as length of the queue of specimens awaiting analysis and contention for resources (e.g., CPU or memory resources), and other factors. The duration of the delay may be a prescribed (fixed) or selectable value. For example, the time-dilation logic may select a delay value from any of various discrete lengths of time (e.g., a 30 minute length, 60 minute length, or two day length) or may select any delay value over a continuous range of available time delays (e.g., 60 minutes to 2 days) and so be fully tunable. The time-dilation logic then adds the delay value to the current time indicated by the clock (e.g., system clock or timestamp) it receives or utilizes, so as to fashion a new, “false” current time for responding to the time request/check from the specimen. Future time requests/checks received while processing the same specimen will take into account the delay introduced during the first response so as to provide a consistent clock and prevent malware from detecting that it is being analyzed (or at least reduce the chances of detection). In some embodiments, the length of delay (increment) may be parameterized and a configuration file may provide values for such time ranges and periods, and be updated, for example, by downloading replacement or modifications to the configuration file from a central source.
Additionally, in one embodiment, the MCD may include a primary controller with configuration, policy, and management logic adapted to communicate with and configure (e.g., provide the rules, policies, and otherwise manage) each of the time controllers. The primary controller may selectively enable/disable one or more of the time controllers to monitor and/or respond to the software calls or other time checks, employing one or more of the techniques mentioned herein. Individual time controllers may be entirely enabled or disabled, or partially enabled or disabled. Partial enablement may denote that the time controller is limited in operation, for example, to introduce only a prescribed and fixed delay time, e.g., a short delay of fixed duration, or to provide time dilation regardless of any observed behaviors of a particular specimen. The primary controller may be implemented, for example, as part of a VM monitor or manager (VMM), also referred to as a hypervisor, which manages VMs, or, in some embodiments, may be functionally integrated into one of the time controllers.
With some of these options and embodiments, a single process (including any and all its threads) in the run-time environment, (e.g., a virtual machine) will see the same length of time dilation, which occurs when the primary controller enables a time controller located for example within that process, proximate the process (e.g., within an independent monitoring process associated with the process), or within any kernel routines servicing the process. In others, a set of processes (e.g., running in a like number of VMs) will see the same length of time dilation, which occurs when the primary controller enables a time controller located for example in the guest OS software for the VM. In still others, the entire virtualized computer (e.g., in one or more VMs) will see a system-wide time dilation (i.e., a delay added to the system clock), which occurs when the primary controller enable a time controller located for example in the hypervisor or the host operating system.
In some embodiments, the primary controller can enable a plurality of time controllers, each with monitoring agent at least in part in a different layer of the functional software stack within the virtual environment. This produces essentially a nesting of enabled time controllers where each may add a time delay relative to the system clock or timestamp so as to produce an aggregated delay that may be greater than that available from any of the time controllers individually. The primary controller may selectively enable one or another (or more than one) of the time controllers (and thus one or more of their respective monitoring agent) so as to “hide” more effectively the time dilation from the malware in analysis. Additionally, in still another embodiment, the primary controller can selectively, and even dynamically (during run-time) associate a time-dilation logic of any of the time controllers with one or more monitoring agents of different time controllers so as to control from any location in which a time-dilation logic is located a number of different monitor agents often located in different layers. This technique may enhance the ability of the MCD to avoid the special defensive strategies employed by some malware to detect that they are located in a malware detection system.
The primary time controller may make decisions as to what technique to employ based on runtime policies or rules. These policies can be static (e.g., factory set), MCD dependent (e.g., dependent on where the MCD is installed in a network), dynamic, e.g., dependent on recent history of malware abilities or trends (e.g., recent known malware remaining inactive for a known number of hours before activation) as detected by the MCD or furnished from other sources, or user set (e.g., by network or security administrators). To illustrate, as a specimen runs, the primary time controller may decide to apply elastic time based on monitored behaviors, and on human experiential knowledge (experienced human analysts) or machine learning (using labelled/known specimens of malware and non-malware and extrapolating to future unknown/unlabeled specimens) to determine optimal time controller configurations.
In one embodiment, the primary time controller formulates and stores an action plan for exercising a specimen so as to trigger activity of malware (if present), and then enables the time controllers to execute the action plan. The action plan may specify using one or more available techniques, one or more available time controllers, specific mappings of time-dilation logic to one or more monitoring agents, and/or specific delay value(s), to cause the process (or the system running the process) to sleep for a period of virtual time and re-awake afterwards to activate malware. This is accomplished while only, e.g., seconds or minutes have passed in real time, instead of, e g., hours or days in “malware time.” The technique and length of time dilation may be selected based on rules, e.g., developed using experiential knowledge of prior malware employing delayed activation techniques, etc.
Accordingly, in some embodiments, each time controller can dilate or shrink the “sleep” time of the specimen so as to control apparent time dilation. This is accomplished, in various embodiments, by a) modifying clocks, b) modifying software call responses, c) updating memory, d) using an alternative kernel scheduler, e) using chipset level delays, e.g., introducing a sleep or hibernation event on a system-wide basis, or f) any combination of the above. Embodiments of the invention may provide a hybrid solution that strives to obtain the benefits of each of these different techniques, may select dynamically from them, and/or dynamically configure them during run time. The dynamic configuration may be based on external configuration sources or on behavior (or lack of behavior) of a specimen under analysis as monitored by the DMC and/or as previously captured for the specimen.
Embodiments of the invention may be embedded in or employed by a dedicated appliance, a network device such as a firewall, a host system, an endpoint or other node of a network, or other electronic device, whether in-line, out-of-band, in production, off-line, or serving as a forensic station or a cloud-based or partially cloud based solution. The operational flexibility provided by these embodiments enables malware to be detected efficiently and without the malware's own detection defense or evasion systems becoming aware of the replacement clock time, thereby increasing the probability that the malware will activate during analysis.
The embodiments of the invention disclosed herein represent significant improvements over traditional techniques for monitoring specimens processed in a VM-based malware detector, and detecting delayed-activation malware.
U.S. Pat. No. 8,635,696 entitled “System and Method of Detecting Time-Delayed Malicious Traffic,” is issued to Ashar Aziz, and commonly assigned with the present case to FireEye, Inc. That patent describes a malware detection system having a controller configured to monitor behavior of a VM in response to processing of network traffic within the VM, and identify anomalous behavior by accelerating activities caused by the network traffic to reduce time for detection. Based on the identified anomalous behavior, the controller determines the presence of time-delayed malicious traffic. The controller accelerates the activities of the network traffic in the VM, for example, by intercepting one or more time-sensitive system calls and modifying one or more responses to the system calls. A known prior art implementation of such technology for monitoring behavior of a VM employs one or more monitors within the guest software image of the VM (and particularly in one or more processes of the operating system), where each monitor is a self-contained entity equipped to intercept time-sensitive system calls and the like, employ its own logic within the same entity to formulate a modified response so as to accelerate activities within the VM, and deliver the modified response to the requester.
Embodiments of the instant invention have many benefits over the known prior art. First, the monitoring logic can be fashioned as a thin or light-weight agent, whose responsibilities are limited to intercepting or trapping software calls or otherwise “hooking” either other time checks or programmatic mechanisms leading to time checks (e.g., breakpoints or exceptions), and delivering responses to the time checks containing an altered time. The logic required to fashion the responses is moved outside the monitoring agent to the time-dilation logic. Second, the two separate entities, that is, the thin monitoring agent and the time-dilation logic, can permit them to be situated in separate layers of the functional stack within or even outside the VM. This combination of features renders the monitoring agent less likely to be detected by malware, achieves more flexibility in detection as to where the components are placed and their view of the behaviors of the content under analysis, enables the provision of coordinated time clocks to deceive malware using a variety of techniques, and provides flexibility in control of responses across monitoring agents or even across multiple time controllers, to list but a few of the advantages further described here.
As noted, the monitoring agent can be placed in a variety of locations in the software stack. Where the monitoring agent is located in the guest image, e.g., in user mode space, and in close proximity to any content processed there, the monitoring agent can obtain specific context information regarding that processing that may be useful in classifying the content as malware. Unfortunately, such location may render the monitoring agent at higher jeopardy of being discovered by the malware, and particularly today's more sophisticated malware that may employ its defense mechanisms to defeat its detection. On the other hand, where the monitoring agent is located farther down the software stack from the content being processed, e.g., in the hypervisor or host operating system layers, some context information may not lend itself to capture by the monitoring agent but the monitoring agent itself may be less prone to discovery by malware. Embodiments of the invention may balance these two factors, with some emphasizing cloaking of the malware detection environment and others emphasizing collection of detection information or faster detection.
In the following description, certain terminology is used to describe features of the invention. For example, in certain situations, both terms “logic” and “engine” are representative of hardware, firmware and/or software that is configured to perform one or more functions. As hardware, logic (or engine) may include circuitry having data processing or storage functionality. Examples of such circuitry may include, but is not limited or restricted to a microprocessor, one or more processor cores, a programmable gate array, a microcontroller, an application specific integrated circuit, wireless receiver, transmitter and/or transceiver circuitry, semiconductor memory, or combinatorial logic.
Logic (or engine) may be in the form of one or more software modules, such as executable code in the form of an executable application, an application programming interface (API), a subroutine, a function, a procedure, an applet, a servlet, a routine, source code, object code, a shared library/dynamic load library, or one or more instructions. These software modules may be stored in any type of a suitable non-transitory storage medium, or transitory storage medium (e.g., electrical, optical, acoustical or other form of propagated signals such as carrier waves, infrared signals, or digital signals). Examples of non-transitory storage medium may include, but are not limited or restricted to a programmable circuit; a semiconductor memory; non-persistent storage such as volatile memory (e.g., any type of random access memory “RAM”); persistent storage such as non-volatile memory (e.g., read-only memory “ROM”, power-backed RAM, flash memory, phase-change memory, etc.), a solid-state drive, hard disk drive, an optical disc drive, or a portable memory device. As firmware, the executable code is stored in persistent storage.
The term “content” generally refers to information transmitted as one or more messages, where each message(s) may be in the form of a packet, a frame, an Asynchronous Transfer Mode “ATM” cell, or any other series of bits having a prescribed format. The content may include one or more types of data such as text, software, images, audio, metadata and/or other digital data. One example of content may include web content, or any data traffic that may be transmitted using a Hypertext Transfer Protocol (HTTP), Hypertext Markup Language (HTML) protocol, or may be transmitted in a manner suitable for display on a Web browser software application.
Another example of content includes electronic mail (email), which may be transmitted using an email protocol such as Simple Mail Transfer Protocol (SMTP), Post Office Protocol version 3 (POP3), or Internet Message Access Protocol (IMAP4). A further example of network content includes an Instant Message, which may be transmitted using Session Initiation Protocol (SIP) or Extensible Messaging and Presence Protocol (XMPP) for example. Yet another example of content includes one or more files that are transferred using a data transfer protocol such as File Transfer Protocol (FTP) for subsequent storage on a file share.
The term “malware” may be construed broadly as any computer code that initiates a malicious attack and/or operations associated with anomalous or undesirable behavior. For instance, malware, as used herein, may correspond to a type of malicious computer code that executes an exploit to take advantage of a vulnerability, for example, to harm or co-opt operation of a network device or misappropriate, modify or delete data. The vulnerability may exist in software running on an electronic device and/or may arise from an action by a person that permits the malware to gain unauthorized access to one or more areas of computer infrastructure such as the electronic device itself or another electronic device or computer resource. Upon gaining access, the malware may carry out its harmful or unwanted action.
The term “transmission medium” is a communication path between two or more systems (e.g. any electronic devices with data processing functionality such as, for example, a security appliance, server, mainframe, computer, netbook, tablet, smart phone, router, switch, bridge or router). The communication path may include wired and/or wireless segments. Examples of wired and/or wireless segments include electrical wiring, optical fiber, cable, bus trace, or a wireless channel using infrared, radio frequency (RF), or any other wired/wireless signaling mechanism.
In general, a “virtual machine (VM)” is a simulation of an electronic device (abstract or real) that is usually different from the electronic device conducting the simulation. VM may be based on specifications of a hypothetical computer or emulate the computer architecture and functions of a real world computer. A VM can be one of many different types such as, for example, hardware emulation, full virtualization, para-virtualization, and operating system-level virtualization virtual machines.
The term “computerized” generally represents that any corresponding operations are conducted by hardware in combination with software and/or firmware.
Lastly, the terms “or” and “and/or” as used herein are to be interpreted as inclusive or meaning any one or any combination. Therefore, “A, B or C” or “A, B and/or C” mean “any of the following: A; B; C; A and B; A and C; B and C; A, B and C.” An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive.
As this invention is susceptible to embodiments of many different forms, it is intended that the present disclosure is to be considered as an example of the principles of the invention and not intended to limit the invention to the specific embodiments shown and described.
Referring to
Herein, according to this embodiment of the invention, first MCD system 1101 is an electronic device that is adapted to use a hybrid (or multi-layer) technique to detect malware that is delaying its own execution to bypass detection by a run-time environment of a malware detection system, established for example as a VM-based virtual environment. Specifically, according to this embodiment, the first MCD system 1101 is adapted to: (i) trap by a time controller monitoring agent 230 located, e.g., in the guest image of the VM, an action (e.g., a software call) by a specimen indicating that the specimen may be seeking to delay its own execution, (ii) communicate by the time controller the trap of the action or call to the time controller time-dilation logic 232, e.g., in the hypervisor or VMM 200, (iii) generate a command or other response to the trapped action so to perform a modification of the time (e.g., dilation or skewing of time) in a predictable manner for a predetermined period of time, (iv) receiving the modified time at the controller monitoring agent 230 and providing the response to the trap to the entity originating the action or call, and (v) processing the specimen with respect to the modified time. Accordingly, the hybrid technique uses both the time controller monitoring agent and the separate time controller time-dilation logic to detect the malicious code by manipulating time.
According to this embodiment of communication system 100, first MCD system 1101 may be a web-based security appliance that is configured to inspect ingress data traffic, identify whether content associated with the data traffic may include malware (“suspiciousness”), and if so, conduct a deeper analysis of the content. This deeper analysis is conducted by processing the content within the VM execution environment to detect undesired behaviors that would be present if the data traffic were actually processed by an electronic device. The particulars of this analysis are described below.
The communication network 130 may include a public computer network such as the Internet, in which case an optional firewall 155 (represented by dashed lines) may be interposed between communication network 130 and client device 150. Alternatively, the communication network 130 may be a private computer network such as a wireless telecommunication network, wide area network, or local area network, or a combination of networks.
The first MCD system 1101 is shown as being coupled with the communication network 130 (behind the firewall 155) via a network interface 160. The network interface 160 operates as a data capturing device (referred to as a “tap”) that is configured to receive data traffic propagating to/from the client device 150 and provide content from the data traffic to the first MCD system 1101.
In general, the network interface 160 receives and copies the content normally without an appreciable decline in performance by the communication network 130. The network interface 160 may copy any portion of the content, for example, any number of data packets. In some embodiments, the network interface 160 may capture metadata from data traffic intended for client device 150, where the metadata is used to determine whether the data traffic includes any suspicious content as well as the software profile for such content. The metadata may be associated with the server device 140 and/or the client device 150. In other embodiments, a heuristic module 170 (described herein) may determine the software profile by analyzing the content associated with the data traffic.
It is contemplated that, for any embodiments where the first MCD system 1101 is implemented as an dedicated computing appliance or a general-purpose computer system, the network interface 160 may include an assembly integrated into the appliance or computer system that includes network ports, network interface card and related logic (not shown) for connecting to the communication network 130 to non-disruptively “tap” data traffic propagating through firewall 155 and provide a copy of the data traffic to the heuristic module 170. In other embodiments, the network interface 160 can be integrated into an intermediary device in the communication path (e.g. firewall 155, router, switch or other network device) or can be a standalone component, such as an appropriate commercially available network tap. In virtual environments, a virtual tap (vTAP) can be used to copy traffic from virtual networks.
Referring still to
In general, the heuristic engine 170 performs static scanning of a file to determine whether objects within the file may include malware or exploits (e.g. portions of content that may contain or be exploited by malware), vulnerable functions, and/or malicious patterns (e.g. shell code patterns, ROP patterns, heap spray patterns, etc.). The term “file” is used for convenience broadly to include network traffic (such as webpages, emails or data flows), executables, non-executables (e.g., documents) and other content. The term “data flow” is used to specify a collection of related packets (e.g., messages) communicated during a single communicated session between a sending and receiving device.
As illustrated in
After static scanning by the heuristic engine 170, the captured content may be presented to the behavioral analysis engine 190 for more in-depth analysis using virtual machine (VM) technology for processing of the file in a run-time environment in which one or more applications are executed (not just emulated). The ensuing processing may be conducted for a period of time that is either fixed or variable, and may be set manually by an administrator or automatically by the MCD system 1101, for example, based on the type of file, queue length of files to be analyzed, or other analysis parameters (such as the results of the heuristic engine 170). For this purpose, the heuristics engine 170 communicates with the scheduler 180.
The scheduler 180 may retrieve and configure a virtual machine (VM) to mimic the pertinent performance characteristics of the client device 150. In one example, the scheduler 180 may be adapted to configure the characteristics of the VM to mimic only those features of the client device 150 that are affected by the data traffic copied by the network interface 160. The scheduler 180 (or the heuristic engine 170, depending on the embodiment) may determine the features of the client device 150 that are affected by the content by receiving and analyzing the network traffic from the network interface 160. Such features of the client device 150 may include ports that are to receive the content, certain device drivers that are to respond to the content, and any other devices coupled to or contained within the client device 150 that can respond to the content. In another embodiment, the scheduler 180 configures the VM with software referred to as a software profile, which is suitable for processing the file. The software profile may include an application, an operating system and a time controller, or at least its monitoring module (see
The behavioral analysis engine 190 may be adapted to execute one or more VMs to simulate the receipt and/or processing of different potentially “malicious” objects within a file under analysis (analyzed file) within a run-time environment as expected by the type of file, as noted above. The run-time environment may be one selected to mimic one that is prevalently provided by client devices, or, in alternative embodiments, one that can be provided by the client device 150 in particular. Furthermore, the behavioral analysis engine 190 analyzes the activities of the file while it is processed in the run-time environment, in other words, the effects of such content upon the run-time environment, such as the client device 150. Such behavior or effects may include unusual network transmissions, unusual changes in performance, and the like. This detection process is referred to as a dynamic malicious content detection. The behavioral analysis engine 190 stores these behaviors or effects in the storage device 185, which may comprise virtual storage.
A classifier 195 is operable to determine, based on the observed or monitored behaviors or effects, whether the file is or contains malware. A reporting module 198 may issue alerts indicating the presence of malware, and may include pointers and other reference information to identify and provide analytical information on the files under analysis. Additionally, the server device 140 may be added to a list of malicious network content providers, and future network transmissions originating from the server device 140 may be blocked from reaching their intended destinations, e.g., by firewall 155 or another device including a malware detection/blocking system. To that end, the MCD may generate a malware indicator reflecting the file contents (e.g., a hash of a portion of the contents), a malicious server identifier, and/or observed characteristics and behavior of the malware.
Further details of embodiments of the behavioral analysis engine 190 and its run-time environment can be had with reference to
Of course, in lieu of or in addition to static scanning operations being conducted by MCD systems 1101-110N, it is contemplated that cloud-based computing services may be implemented to perform such static scanning: deconstruct a file for static scanning; conduct static scans on one or more objects within the deconstructed file(s); and/or perform emulation on any of these objects, as described herein. Alternatively, the dynamic analysis performed by the behavioral analysis engine 190 together with malware classification and reporting may be provided by cloud-based computing services. In accordance with this embodiment, MCD system 1101 may be adapted to establish secured communications with cloud-based computing services for exchanging information.
Referring now to
Each VM disk file (e.g., VM disk file 2601) comprises information, including (i) profile information 270 and (ii) state information 275, along with a persistent event log file 280. Event log file 280 is adapted to persistently store certain actions, activities or behaviors (called “events”) monitored during processing of the suspicious content 220 during execution of a VM based on the software profile 270. The terms “activity”, “action” and “behavior” when referring to detected processing events may be used interchangeably in this description of embodiments of the invention.
Herein, as illustrated, profile information 270 includes information directed to identified items forming the software profile within VM disk file 2601 from which a corresponding VM is instantiated. Examples of items within the software profile may include, but are not limited or restricted to a particular OS type/version; type(s)/version(s) of application(s) and state information 275. Additionally, the profile information 270 may contain information regarding location of time controllers (including the monitors and time-dilation logic) within the run-time environment.
The storage device 185 may further include, a system clock 408 and virtual chipset 411 including, in some embodiments, a virtual CPU) instantiated in or capable of being supported by a corresponding VM. In some embodiments, the system clock 408 may be contained in the virtual chipset 411.
According to one embodiment of the invention, when suspicious content 220 is received for dynamic analysis (as opposed to static analysis conducted by heuristic engine 170), scheduler 180 of detection controller 200 is configured to identify and select one or more VMs 2101-210N in which the suspicious content 220 is to be analyzed. The targeted operating environment is identified by software profile information (e.g., particular versions of OS and application images along with information directed to the virtual device states). The targeted operating environment may also include a time controller, or at least its monitoring module 230, as described below.
More specifically, the scheduler 180 comprises VM provisioning logic 240 and VM resource logic 245. VM provisioning logic 240 is responsible for creating and provisioning VMs. VM resource logic 245 operates as the centralized logic within the MCD system for responding to resource requests, allocating resources, and monitoring the allocation of such resources.
Upon receipt of the software profile by the behavioral analysis engine 190, the scheduler 180 launches a VM 2101 in which a monitoring module 230 may be running and configured to monitor activities of suspicious content 220 useful in determining whether the incoming content includes malware and whether the particular software profile has any vulnerabilities that are being exploited. In addition, monitoring module 230 maintains a persistent communication channel with event log 250 of detection controller 200 to communicate certain monitored behaviors (events) of suspicious content 220 during execution of VM 2101.
In response to detecting certain events during processing of suspicious content 220, the monitoring module 230 is configured to send a message via the communication channel to event log 250, where the message may be persistently recorded as part of event log file 280. The message may include information identifying the event. Event log 250 records events that have been selectively monitored and detected by monitoring module 230, including undesired behaviors. The recordation of the events may be prompted in response to a particular action (e.g., file creation, registry access, and process execution). The recorded events may be subsequently evaluated by classifier 195 (
As discussed above, in order to capture the behavior of the suspicious content's delayed action, the VM 2101 may manipulate time such that the suspicious content believes that the manipulated time (that is greater than the real time) has elapsed. Accordingly, the suspicious content will mistakenly believe that the action has been delayed for the desired amount of time and execute the action which may be captured by the VM 2101. In some embodiments, parts of the time controllers located in multiple layers of the MCD system 1101 are used to create a hybrid (multi-layer) techniques that manipulates time to capture the suspicious content's delayed action.
Referring to
In
The time controller 305 may be configured by runtime policies. Accordingly, the time controller 305 may be entirely enabled or disabled, may be partially enabled or disabled, and may be dynamically configured in accordance to the runtime policies. In some embodiments, the time controller may be dynamically configured based on (i) configuration sources that are external to the VM or (ii) the detection of behavior or lack of behavior by the specimen being assessed inside the VM. Configuring the time controllers 305 can be performed by an administrator or security personnel via a user interface or, in other embodiments, by primary controller (
In one embodiment, the MCD system 1101 illustrated in
The objective of the “time bomb” types of malware is to delay completion of execution of its malicious code so they may avoid detection or activate at an opportune time, and they may employ a number of different approaches to achieving this. These may include: (1) making an software (e.g., API) call that is transferred from the guest user mode layer to the guest kernel mode layer for handling, (2) including assembly code that requests the number of clock cycles (ticks) directly from the CPU or the virtual CPU included in the virtual chipset, and (3) making a software call (e.g., library call) serviced in the guest user mode layer. These three delay approaches will now be detail along with the techniques employed by embodiments of the invention in dealing with them to facilitate detecting time-delayed malware.
(1) API Call Transferred from the Guest User Mode Layer to the Guest Kernel Mode Layer
In the first delay approach known to be used by malware, the malware makes an API call to delay execution of its malicious code. The API call may be, for instance, sleep, wait for a single object, wait for multiple objects, or another call that serves to signal the task scheduler to tell the OS that it is going to run a code in the future, etc. The API calls may also be effected by lower level calls specific to an operating system such as, for example, delay execution, or yield. Generally speaking, using these time-based API calls, the malware instructs the infiltrated computing device not to run/execute its malicious code for a period of time. These API calls are transferred into the kernel mode layer such that they become system calls.
(2) Assembly Code that Makes Time-Related Requests Directly from the Virtual Chipset
In the second delay approach, the malware may include opcodes (or machine code) to effect a delay. For example, the malware may use a low level machine instruction call such as the opcode “rdtsc” (read time stamp counter instruction) to obtain the number of ticks that represents the number of clock cycles on the CPU since the machine booted. A malware author may use this opcode in the malware to delay execution of its malicious payload without using API or system calls by making requests directly to the CPU from time to time for the number of ticks and waiting for a prescribed incremental increase in the number of ticks before executing. For instance, the malware may (i) request the number of ticks using the opcode, (ii) perform a Sleep or similar operation, (iii) request the number of ticks using the opcode at a later time, and (iv) execute its malicious code if the number of ticks indicates that a sufficient amount of delay has passed (e.g., malware's desired delay has been reached).
(3) API Call Serviced in the User Mode Layer
In this third delay approach, the malware makes an API call that is not transferred to the kernel mode layer but rather is serviced by another mechanism. For example, this API call may be serviced in memory at the Guest user mode layer. One example of an API call that does not require a system call is Get Tick Count. In some embodiments, the malware may pursue an “in memory inspection” approach to achieve the same effect as such API call (e.g., get tick count) by assessing and copying portions of assembly code of the OS used for responding to such API calls into its own malicious code or into the process running the malware.
In order to address each of the delay approaches described above that are often employed by malware to delay execution of its malicious payload, embodiments of the invention manipulate time to make the malware believe that the malware's desired time delay has been reached. Some embodiments of the invention make use of the different layers in software stack 300 of the MCD system 1101 to create a hybrid technique.
In the first approach for effecting a delay where the malware (or the process in which it is running) makes an API call that is transferred from the user mode layer to the kernel mode layer (e.g., transitioning to the system call), the API call returns when it has been serviced from the kernel to the user mode layer and to the malware. The API call can be intercepted, trapped, or otherwise “hooked” in either the user mode layer or the kernel mode layer by monitoring agent 330 located appropriately, e.g., proximate one or the other of the user mode layer 301 or kernel mode layer 302, respectively. In order to be serviced by the kernel, the API call transitions to a kernel scheduler, e.g., scheduler 306, which is located in the kernel mode layer 303. In an example embodiment, the time-dilation logic 232 corresponding to the monitoring agent 230 that intercepts the API call is located in the hypervisor layer 303. The hypervisor 200 communicates a confirmation back to the kernel mode layer 302 and/or the user mode layer 301 with an altered time seen by the Guest OS. In another embodiment, the time-dilation logic 232 of the time controller 305 running in the hypervisor layer 303 in association with the hypervisor 200 may communicate the confirmation. Alternatively, a time controller 305 included in the virtual chipset 411 (e.g., virtual CPU) modifies the response to a software call (or other time check), and generates an interrupt (e.g., a clock interrupt) that is received by the kernel scheduler 306 of the kernel mode layer 302 to alter the Guest OS time
A further understanding of the inventive techniques to address the approaches commonly used by delayed activation malware can be had with reference to the flowcharts of
At Block 503, the action logic in the hypervisor layer may manipulate the time of a clock of the guest OS. The manipulated time causes the predetermined delay period to elapse faster than in real time. For example, where the API call is serviced in the kernel mode layer, the action logic of the hypervisor layer may change how the kernel scheduler runs in order to manipulate the time of the clock of the guest OS. For instance, the action logic, using interrupts sent to the kernel scheduler, may generate a time adjustment or may change the clock to cause the kernel scheduler to move forward in time. Accordingly, by altering the time seen by the kernel scheduler, the timing of the whole guest OS is being altered. This may reduce the chance of the malware discovering that time is being manipulated by a malware detection system.
In some embodiments, to generate the time adjustment, action logic uses the interrupts to add an increment to each clock cycle or to change the frequency of the clock cycles.
If the malware uses the second approach for effecting delay—where the malware includes opcodes, e.g., for “rdtsc,” to obtain directly the number of CPU ticks, and then delay until a prescribed tick count has been reached—embodiments of the invention may employ the virtual chipset to provide a manipulated number of hardware ticks that represents an exaggerate (increase) of the number of clock cycles that have been seen by the CPU. In some embodiments, the virtual clock included in the virtual chipset is provided the same incremental or same frequency change that is provided to the guest OS such that the virtual clock may provide a manipulated number of ticks in response to the opcode “rdtsc”.
For this, the rdtsc opcode may be trapped explicitly in some embodiments by a monitoring agent in the hypervisor layer in response to a monitored Sleep or other delay request detected by a monitoring agent located in a higher layer such as the guest user mode process or guest kernel mode layer. In response, time-dilation logic located in the hypervisor layer or the guest kernel layer may provide a false time by moving forward a timestamp counter of the virtual chipset (e.g., the virtual CPU) read by the rdtsc, which false time is then provided as a response to the rdtsc opcode to the malware. In alternative embodiments to those just described, the timestamp counter may be moved forward to provide a false time in response to subsequent rdtsc opcode without the need to trap the rdtsc, in response to a monitored delay in execution of an object or process or a period of predetermined length of inactivity with respect to an object or process being executed.
In another embodiment, rather than generating interrupts from the hypervisor layer or chipset to the kernel scheduler 306 to manipulate the time of the guest OS, an alternate kernel scheduler 307 is included in the kernel layer. The alternate kernel scheduler 307, which may be substantially identical to the kernel scheduler 306, receives the interrupts from the hypervisor layer that cause the alternate kernel scheduler 307 to manipulate the clock time, as discussed above. In this embodiment, when the hypervisor layer receives the communication from the user mode layer or the kernel mode layer that a manipulation of time is desired to trap a “time bomb” malware (Block 502), the hypervisor layer may signal activation of the alternate kernel scheduler in lieu of operation of the otherwise active kernel scheduler for this process (Block 503).
In another embodiment, a hybrid technique may perform a secondary trap on the “time bomb” malware. As discussed above, when the monitoring agent in the user mode layer or the kernel mode layer identifies a suspicious content that is trying to delay (e.g., for three hours) execution of its malicious code (Block 501), the monitoring agent communicates the trap to the hypervisor layer (Block 502). In this embodiment at Block 503, the hypervisor may cause the virtualized chipset to generate a hibernation to bring the system “down” and communicate that a hibernation (equal to the delay) is set. However, once the system is in hibernation, the action logic may alter the clock to move time forward (e.g., change the time to reflect a three-hour advance in time when only five seconds have passed). The hypervisor may signal to the virtual chipset to alter the number of hardware ticks in the virtual chipset to reflect a manipulated time (e.g., moving time forward by increasing the number of hardware ticks). In this embodiment, the virtual machine 2101 may leverage some of the non-interrupt chipset capabilities to virtualize the hibernate (or sleep) and assess how the guest OS handles the resumption of processing (resume) after the hibernation and the adjustment of the clock. The virtual machine 2101 may thus, leverage built-in OS features to alter time in such a way that time is moving forward convincingly to the malware.
In this embodiment, in the event that the malware performs a subsequent check to determine if the hibernation was in fact three hour in duration and not a manipulated time that was effectuated to trap its malicious code, the action logic may identify the potential malware as a time bomb based on the malware's API call for a delay in execution of its code in conjunction with the subsequent checks being performed by the potential malware that regarding the delay.
While the time controller may be triggered to trap the malware and manipulate time by malware behavior (or lack of behavior) within the VM as discussed above (e.g., API calls such as “to sleep”, memory changes such as “Get tick count”, CPU opcodes such as “rdtsc”, etc.), the time controller may also be triggered by means that are external to the VM. For instance, the detector logic in the MCD system 1101 may detect “time bomb” malware based on assessing a sequence or group of events and determining that this sequence or group of events is seeking to modify time. For instance, rules applied on the dynamic sequence or group of events may cause the detector logic to enable the time controller to trap the malware and manipulate time while the specimen is running. Accordingly, the time controller may perform one or more methods to manipulate the time to cause the specimen to see a shorter time period than the actual time elapsed or may cause the specimen to run at a future date. In another example, the MCD system 1101 may process a specimen simultaneously or in parallel (in a temporally overlapping manner) in VMs with multiple guest profiles and with different time controller configurations: (i) a first profile may be configured with specified monitoring agents completely disabled or partially disabled and (ii) a second profile may be configured with the monitoring agent enabled. Using the parallel specimen submissions, the MCD system 1101 may create a dynamic learning system for human analysis or for machine learning. The MCD system 1101 as well as the time controller may thus learn the malware's time bomb behavior and assess the best methods to manipulate time in other (future) MCD analysis to thwart the malware's “time bomb” attack.
At Block 702, a time-dilation logic of the time controller included in a second layer of the functional stack of the run-time environment supplies in response to the time-related check a false time that is later than real time so as to indicate a greater length of time has transpired than has actually transpired.
At Block 703, the monitoring agent monitors activity of the process in the run-time environment in response to the supplied false time to detect anomalous behavior indicative of malware
In some embodiments, the time-dilation logic of the time controller may also manipulate real time to generate the false time and cause a predetermined period of time of execution of the process to elapse faster than in real time.
In one embodiment, the monitoring agent in the kernel mode layer communicates the trapping of the time-related check from to the time-dilation logic located at least in part in a hypervisor layer of the virtual machine, and the time-dilation logic manipulates a time of a clock of the guest OS to yield the false time, and cause a predetermined delay period in the guest OS to elapse faster than in real time. In this embodiment, the manipulating of the time of the clock of the guest OS includes altering an interrupt sent to a kernel scheduler included in the kernel mode layer and to cause the kernel scheduler to initiate processing of the specimen within a desired period of time. As discussed in the embodiments above, the altered interrupt transmitted to the kernel scheduler may cause the kernel scheduler to advance the time of the clock of the guest OS to reflect the false time. For instance, the altered interrupt may cause the kernel scheduler to perform at least one of: increase a frequency of the clock of the guest OS, and increment the clock of the guest OS with a value greater than that normally added thereto. In one embodiment, the kernel scheduler may include a first scheduler and an alternate scheduler, and the kernel scheduler initiates processing of the specimen within the desired period of time using the alternate kernel scheduler included in the kernel mode layer to schedule processing of the specimen. In this embodiment, to manipulate the time of the clock in the guest OS, the time-dilation logic of the time controller may also include partially or completely disable the first kernel scheduler, and enable the alternate scheduler. In another embodiment, the altered interrupt transmitted to the alternate kernel scheduler causes the alternate kernel scheduler to advance the time of the clock of the guest OS to reflect the false time.
In one embodiment, the monitoring agent may trap the time check by trapping an API call that is serviced in the kernel mode layer. In another embodiment, the monitoring agent may trap the time check that comprises a CPU query of a system clock. In this embodiment, the monitoring agent may supply the false time by executing an opcode requesting a number of hardware ticks, reflecting the system clock in an operating system clock, and providing the false time based on the operating system clock in response to the CPU query.
The controller may also direct the CPU query to a virtual CPU of a virtual machine executing in the user mode level of the run-time environment. In this embodiment, providing the false time by the time-dilation logic comprises directing the response to an executing software module in the user mode level. The time-dilation logic may also supply the false time by advancing a “virtual” system clock maintained by a virtual chipset by a number of ticks, and generating the false time based on the system clock.
In another embodiment, the monitor agent traps the time-related check by trapping a memory access corresponding to a time query made to a first memory location corresponding to the process, and the time-dilation logic supplying the false time by altering a mapping of memory at the user mode layer corresponding to the process so as to provide in response to the memory access, contents from a second memory location. The first memory location may include contents that reflect the real time and the second memory location contents may reflect the false time.
According to one embodiment, the memory at the user mode layer is virtual and mapped by a kernel mode layer for every process to a physical area of memory included in a physical layer. In this example, the monitor agent trapping a memory access corresponding to a time query includes trapping an API call that is capable of being serviced in memory at the user mode layer.
Each monitoring agent 9011-901n and action logic 902 of a time controller 305 may reside inside or be operative in association with one of the following: (i) the guest image (e.g., the specimen or process running in the user space or kernel space of the guest image), (ii) the host OS software, (iii) the hypervisor, or (iv) a virtualized device of the VM. Alternatively, monitoring agent 901 in
Each time controller can be referred to as an “elastic” time controller since its action logic may be configured to establish “short” or “long” delays. In other words, the action logic of a time controller can respond to a software call from a specimen being processed with a response containing a selected “false” time that induces the specimen into believing it has been dormant or inactive for a shorter period of time or a longer period of time depending on detection needs.
The action logic 902 can decide on the length of delay based on prior analysis results, metadata regarding the specimen, identity of processes running in the run-time environment, experiential knowledge in dealing with known malware, current analysis parameters such as length of the queue of specimens awaiting analysis and contention for resources (e.g., CPU or memory resources), and other factors. The duration of the delay may be a prescribed (fixed) or selectable value. For example, the action logic may select a delay value from any of various discrete lengths of time (e.g., a 30 minute length, 60 minute length, or two day length) or may select any delay value over a continuous range of available time delays (e.g., 60 minutes to 2 days) and so be fully tunable. The action logic 902 then adds the delay value to the current time indicated by the clock it receives or utilizes, so as to fashion a new, “false” current time for replying to the time request/check from the specimen. Future time requests/checks received while processing the same specimen will take into account the delay introduced during the first response so as to provide a consistent clock and avoid malware from detecting that it is being analyzed. In some embodiments, the length of delay (increment) may be parameterized and a configuration file may provide values for such time ranges and periods, and be updated, for example, by downloading replacement or modifications to the configuration file from a central source.
In one embodiment, an exploit detection system (or MCD system 1101) may be described as comprising a run-time environment, a behavior controller (e.g., time controller 305) and an analyzer (e.g., behavioral analysis engine 190). The run-time environment that is configurable to execute executable computer program components may process a specimen. As illustrated in
The analyzer receives the monitor results from the monitoring agent and detects an exploit, if the monitor results indicate that there is an exploit. In another embodiment, the analyzer is further configured to capture and report indicators of compromise including the monitor results associated with the detected exploit.
According to one embodiment, the exploit detection system may also include a virtual machine, a hypervisor to manage the virtual machine, and a host operating system over which the virtual machine and hypervisor execute. The virtual machine may run a guest application in which the specimen is processed, and a guest operating system over which the application executes. In this embodiment, the virtual machine, guest application, guest operating system, hypervisor, and host operating system comprise the executable computer program components.
In one embodiment, the run-time environment further processes the specimen responsive to a configurable run-time parameter and the action logic configures the run-time parameter so as to influence processing of the specimen in response to the monitoring results. The action logic may be responsive to a stored action plan that includes a plurality of rules identifying values for run-time parameters to be provided by the action logic to the run-time environment in response to a plurality of different pre-determined sets of potential monitor results.
Additionally, in one embodiment, the MCD 1101 may include a primary controller with configuration, policy, and management logic situated in one or more of these multiple layers and adapted to communicate with and configure (e.g., provide the rules, policies, and otherwise manage) each of the time controllers. The primary controller may selectively enable/disable one or more of the time controllers to monitor and/or respond to the software calls or other time checks, employing one or more of the techniques mentioned herein. Individual time controllers may be entirely enabled or disabled, or partially enabled or disabled. Partial enablement may denote that the time controller is limited in operation, for example, to introduce only a prescribed and fixed delay time, e.g., a short delay of fixed duration, or to provide time dilation regardless of any observed behaviors of a particular specimen.
The primary controller may be implemented as part of a VM monitor or manager (VMM), also referred to as a hypervisor, which manages or monitors VMs, or, in some embodiments, may be integrated functionally into one of the time controllers. In one embodiment, the MCD may be equipped with the primary controller, e.g., as part of the VMM, when the MCD is deployed, which enables or disables a specific time controller (or specific monitoring agent or action logic) instantiated with a corresponding virtual machine based on detection requirements.
In one embodiment, the MCD system to detect delayed activation malware includes a plurality of time controllers 3051-305m including a first time controller 3051 and a second time controller 3052. The MCD system also includes a run-time environment to be executed by a processor, and that includes an application to process one or more specimens. The one or more specimens may or may not include delayed activation malware. The run-time environment also includes an operating system in communication with the application, and a plurality of monitors including the monitoring agents of the first time controller 3051 and second time controller 3052, respectively. The monitoring agents are configured so as to be operable in association with different components of the run-time environment to trap, directly or indirectly, a time-related check of the run-time environment observed from the associated component. The action logic of the first and second time controllers 3051, 3052 respond to the time-related check with a false time that is later than real time so as to indicate a greater length of time has transpired than has actually transpired. During execution, the application processes the one or more specimens based at least in part on the false time and the monitoring agents of the run-time environment monitor activity of the application during execution to detect whether the delayed activation malware is included in the one or more specimens. The monitoring agents may thus assess whether anomalous activity is associated with the malware.
In one embodiment, the monitoring agent of the first time controller 3051 operates in association with a first component of the run-time environment comprising the application while the monitoring agent of the second time controller 3052 operates in association with a second component of the run time environment comprising the operating system. Accordingly, the MCD system may trap time checks originating from the application and the operating system, respectively. The application may use a clock reflecting the false time, and the operating system may use a clock reflecting the real time. In one embodiment, the application and the operating system may reside in the guest memory space and use a clock reflecting the false time while the host operating system accesses and uses a clock reflecting the real time.
The run-time environment may comprise a virtual run-time environment that includes a hypervisor, and at least one virtual machine in communication with the hypervisor. In one embodiment, the monitoring agent of the first time controller 3051 operates in association with a third component of the run-time environment that includes either the application or the operating system while the monitoring agent of the second time controller 3052 operates in association with a fourth component of the run time environment that includes the hypervisor. According, this embodiment of the MCD 101 is able to trap time checks originating from the third component and the fourth component, respectively.
In another embodiment, the run-time environment also includes a host operating system. The monitoring agent of the first time controller 3051 in this embodiment operates in a guest memory space of the host operating system while the monitoring agent of the second time controller 3052 operates in association with a kernel memory space of the host operating system.
In one embodiment, the action logic 902 of each of the first and second time controllers 3051, 3052 provides a false time in response to a software call from the application comprising the time-related check. In another embodiment, the action logic 902 of each of the first and second time controllers 3051, 3052 determines a delay period for purposes of time dilation to be used in generating the false time.
Referring back to
The primary controller 305m may be configured to enable both the first and second time controllers 3051, 3052. In this embodiment, the action logic of the first and second time controllers 3051, 3052 are configured to provide time dilation action when enabled such that the false time reflects both the time dilation actions of the first and second time controllers 3051, 3052.
When the action logic of the first and second time controllers 3051, 3052 determine a delay period to be used in generating the false time, the primary controller 305m configures the first and second time controllers 3051, 3052 with runtime rules for the first and second time controllers 3051, 3052 to use in determining the delay period. The run-time rules may be pre-set or dynamically determined during run-time based on one or more factors including history of activation delays of known delayed activation malware.
The primary controller 305m generates and stores in a memory of the MCD an action plan for exercising the one or more specimens so as to trigger activity of malware that may be included therein. The primary controller 305m may also configure the first and second time controllers 3051, 3052 to execute the action plan that specifies an identifier of each of the time controllers 3051, 3052 to be enabled, and specific delay values to be reflected in the false time to cause the application to execute within a desired period of time.
In one embodiment, the trapping of a time-related check in Block 803 may include trapping time checks originating from the application and the operating system by operating the monitoring agent of the first time controller in association with the application, and operating the monitoring agent of the second time controller in association with the operating system. In another embodiment, the trapping of a time-related check in Block 803 may include trapping time checks originating from a first component and a second component of the run-time environment by operating the monitoring agent of the first time controller in association with the first component comprising one of the application and the operating system, and operating the monitoring agent of the second time controller in association with the second component comprising the hypervisor.
In one embodiment, the application may use a clock reflecting the false time while the operating system may use a clock reflecting the real time. In another embodiment, the application and the operating system may use a clock reflecting the false time while the host operating system uses a clock reflecting the real time.
In one embodiment, an exploit detection system (or MCD system 1101) may be described as comprising a run-time environment, a plurality of behavior controllers (e.g., time controllers 3051-305m as shown in
In one embodiment, the executable computer program components include a virtual machine, a hypervisor configured to manage the virtual machine, and a host operating system over which the virtual machine and hypervisor execute. The virtual machine runs a guest application in which the specimen is processed and a guest operating system over which the application executes. In one embodiment, the virtual machine, guest application, guest operating system, hypervisor, and host operating system comprise the executable computer program components.
In one embodiment, the run-time environment processes the specimen responsive to a configurable run-time parameter, and the time-dilation logic of the behavior controllers configures the run-time parameter so as to influence processing of the specimen in response to the monitoring results. The action plan may include a plurality of rules identifying which of the monitoring agents are to be enabled or disabled, and values for run-time parameters to be provided by the time-dilation logic to the run-time environment in response to a plurality of different pre-determined sets of potential monitor results.
In one embodiment, the primary controller 305m comprises the action logic of at least one of the behavior controllers. In other words, the behavior controllers include the monitoring agent 901 but the action logic 902 that is coupled to the behavior controllers' monitoring agent 901 is included in the primary controller 305m. In another embodiment, each of the monitoring agents monitors activities within a corresponding first of the executable components that is different from the executable component corresponding to another of the monitoring agents.
The memory 1206 may include a plurality of locations that are addressable by the CPU(s) 1202 and the network interface(s) 1204 for storing software program code (including application programs) and data structures associated with the embodiments described herein. The CPU 1202 may include processing elements or logic adapted to execute the software program code, such as that of the software stack 300 illustrated in
A host operating system kernel 1212, portions of which are typically resident in memory 1206 and executed by the CPU 1202, functionally organizes the computing apparatus 1200 by, inter alia, invoking operations in support of the application programs executing on the computing apparatus 1200. A suitable host operating system kernel 1212 may include the Windows® series of operating systems from Microsoft Corp of Redmond, Wash., the MAC OS® and IOS® series of operating systems from Apple Inc. of Cupertino, Calif., the Linux operating system, among others. Suitable application programs 1214 stored in memory 1206 may include proprietary software programs of FireEye, Inc., and commercially available software programs. Illustratively, the application programs 1214 may be implemented as user mode processes of the kernel 230. As used herein, a process (e.g., a user mode process) is an instance of software program code (e.g., an application program) executing in the operating system that may be separated (decomposed) into a plurality of threads, wherein each thread is a sequence of execution within the process. The memory 1206 also includes a data repository for storing various data and metadata as discussed hereinabove.
It can now be seen that the described embodiments of the invention seek to deceive delayed-activation malware by using time-dilation techniques to modify responses to time-related software calls and other “inquiries” undertaken by such malware. Aspects of the invention can also apply to detect other forms of malware so as to hide the fact that the malware is being processed within a malware detector or sensor. For example, where malware performs checks on IP addresses or other identifying information associated with a computing device on which it is being processed, these embodiments may provide false information in response to the checks (“modified response”) so as to deceive the malware as to where it is being processed. For these purposes, a more generalized vocabulary is useful: an action controller (such as, for example, the above-described time controller) may include one or more monitoring agents and an action logic (such as, for example, the above-described time-dilation logic).
It will be apparent to those skilled in the art that other types of processing elements and memory, including various computer-readable media, may be used to store and execute program instructions pertaining to the embodiments described herein. Also, while the embodiments herein are described in terms of software program code and computer, e.g., application, programs stored in memory, alternative embodiments also include the code or logic embodied as modules consisting of hardware, software, firmware, or combinations thereof.
In the foregoing description, the invention is described with reference to specific example embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention as set forth in the appended claims. For instance, in lieu of or in addition to the MCD system 1101-1103 of
This application claims the benefit of priority on U.S. Provisional Application No. 62/235,494 filed Sep. 30, 2015, the entire contents of which are incorporated by reference.
Number | Name | Date | Kind |
---|---|---|---|
4292580 | Ott et al. | Sep 1981 | A |
5175732 | Hendel et al. | Dec 1992 | A |
5319776 | Hile et al. | Jun 1994 | A |
5440723 | Arnold et al. | Aug 1995 | A |
5490249 | Miller | Feb 1996 | A |
5657473 | Killean et al. | Aug 1997 | A |
5802277 | Cowlard | Sep 1998 | A |
5842002 | Schnurer et al. | Nov 1998 | A |
5960170 | Chen et al. | Sep 1999 | A |
5978917 | Chi | Nov 1999 | A |
5983348 | Ji | Nov 1999 | A |
6088803 | Tso et al. | Jul 2000 | A |
6092194 | Touboul | Jul 2000 | A |
6094677 | Capek et al. | Jul 2000 | A |
6108799 | Boulay et al. | Aug 2000 | A |
6154844 | Touboul et al. | Nov 2000 | A |
6269330 | Cidon et al. | Jul 2001 | B1 |
6272641 | Ji | Aug 2001 | B1 |
6279113 | Vaidya | Aug 2001 | B1 |
6298445 | Shostack et al. | Oct 2001 | B1 |
6357008 | Nachenberg | Mar 2002 | B1 |
6424627 | Sorhaug et al. | Jul 2002 | B1 |
6442696 | Wray et al. | Aug 2002 | B1 |
6484315 | Ziese | Nov 2002 | B1 |
6487666 | Shanklin et al. | Nov 2002 | B1 |
6493756 | O'Brien et al. | Dec 2002 | B1 |
6550012 | Villa et al. | Apr 2003 | B1 |
6775657 | Baker | Aug 2004 | B1 |
6831893 | Ben Nun et al. | Dec 2004 | B1 |
6832367 | Choi et al. | Dec 2004 | B1 |
6895550 | Kanchirayappa et al. | May 2005 | B2 |
6898632 | Gordy et al. | May 2005 | B2 |
6907396 | Muttik et al. | Jun 2005 | B1 |
6941348 | Petry et al. | Sep 2005 | B2 |
6971097 | Wallman | Nov 2005 | B1 |
6981279 | Arnold et al. | Dec 2005 | B1 |
7007107 | Ivchenko et al. | Feb 2006 | B1 |
7028179 | Anderson et al. | Apr 2006 | B2 |
7043757 | Hoefelmeyer et al. | May 2006 | B2 |
7058822 | Edery et al. | Jun 2006 | B2 |
7069316 | Gryaznov | Jun 2006 | B1 |
7080407 | Zhao et al. | Jul 2006 | B1 |
7080408 | Pak et al. | Jul 2006 | B1 |
7093002 | Wolff et al. | Aug 2006 | B2 |
7093239 | van der Made | Aug 2006 | B1 |
7096498 | Judge | Aug 2006 | B2 |
7100201 | Izatt | Aug 2006 | B2 |
7107617 | Hursey et al. | Sep 2006 | B2 |
7159149 | Spiegel et al. | Jan 2007 | B2 |
7213260 | Judge | May 2007 | B2 |
7231667 | Jordan | Jun 2007 | B2 |
7240364 | Branscomb et al. | Jul 2007 | B1 |
7240368 | Roesch et al. | Jul 2007 | B1 |
7243371 | Kasper et al. | Jul 2007 | B1 |
7249175 | Donaldson | Jul 2007 | B1 |
7287278 | Liang | Oct 2007 | B2 |
7296271 | Chalmer et al. | Nov 2007 | B1 |
7308716 | Danford et al. | Dec 2007 | B2 |
7328453 | Merkle, Jr. et al. | Feb 2008 | B2 |
7346486 | Ivancic et al. | Mar 2008 | B2 |
7356736 | Natvig | Apr 2008 | B2 |
7386888 | Liang et al. | Jun 2008 | B2 |
7392542 | Bucher | Jun 2008 | B2 |
7418729 | Szor | Aug 2008 | B2 |
7428300 | Drew et al. | Sep 2008 | B1 |
7441272 | Durham et al. | Oct 2008 | B2 |
7448084 | Apap et al. | Nov 2008 | B1 |
7458098 | Judge et al. | Nov 2008 | B2 |
7464404 | Carpenter et al. | Dec 2008 | B2 |
7464407 | Nakae et al. | Dec 2008 | B2 |
7467408 | O'Toole, Jr. | Dec 2008 | B1 |
7478428 | Thomlinson | Jan 2009 | B1 |
7480773 | Reed | Jan 2009 | B1 |
7487543 | Arnold et al. | Feb 2009 | B2 |
7496960 | Chen et al. | Feb 2009 | B1 |
7496961 | Zimmer et al. | Feb 2009 | B2 |
7519990 | Xie | Apr 2009 | B1 |
7523493 | Liang et al. | Apr 2009 | B2 |
7530104 | Thrower et al. | May 2009 | B1 |
7540025 | Tzadikario | May 2009 | B2 |
7546638 | Anderson et al. | Jun 2009 | B2 |
7565550 | Liang et al. | Jul 2009 | B2 |
7568233 | Szor et al. | Jul 2009 | B1 |
7584455 | Ball | Sep 2009 | B2 |
7603715 | Costa et al. | Oct 2009 | B2 |
7607171 | Marsden et al. | Oct 2009 | B1 |
7639714 | Stolfo et al. | Dec 2009 | B2 |
7644441 | Schmid et al. | Jan 2010 | B2 |
7657419 | van der Made | Feb 2010 | B2 |
7676841 | Sobchuk et al. | Mar 2010 | B2 |
7698548 | Shelest et al. | Apr 2010 | B2 |
7707633 | Danford et al. | Apr 2010 | B2 |
7712136 | Sprosts et al. | May 2010 | B2 |
7730011 | Deninger et al. | Jun 2010 | B1 |
7739740 | Nachenberg et al. | Jun 2010 | B1 |
7779463 | Stolfo et al. | Aug 2010 | B2 |
7784097 | Stolfo et al. | Aug 2010 | B1 |
7832008 | Kraemer | Nov 2010 | B1 |
7836502 | Zhao et al. | Nov 2010 | B1 |
7849506 | Dansey et al. | Dec 2010 | B1 |
7854007 | Sprosts et al. | Dec 2010 | B2 |
7869073 | Oshima | Jan 2011 | B2 |
7877803 | Enstone et al. | Jan 2011 | B2 |
7904959 | Sidiroglou et al. | Mar 2011 | B2 |
7908660 | Bahl | Mar 2011 | B2 |
7930738 | Petersen | Apr 2011 | B1 |
7937387 | Frazier et al. | May 2011 | B2 |
7937761 | Bennett | May 2011 | B1 |
7949849 | Lowe et al. | May 2011 | B2 |
7996556 | Raghavan et al. | Aug 2011 | B2 |
7996836 | McCorkendale et al. | Aug 2011 | B1 |
7996904 | Chiueh et al. | Aug 2011 | B1 |
7996905 | Arnold et al. | Aug 2011 | B2 |
8006305 | Aziz | Aug 2011 | B2 |
8010667 | Zhang et al. | Aug 2011 | B2 |
8020206 | Hubbard et al. | Sep 2011 | B2 |
8028338 | Schneider et al. | Sep 2011 | B1 |
8042184 | Batenin | Oct 2011 | B1 |
8045094 | Teragawa | Oct 2011 | B2 |
8045458 | Alperovitch et al. | Oct 2011 | B2 |
8069484 | McMillan et al. | Nov 2011 | B2 |
8087086 | Lai et al. | Dec 2011 | B1 |
8171553 | Aziz et al. | May 2012 | B2 |
8176049 | Deninger et al. | May 2012 | B2 |
8176480 | Spertus | May 2012 | B1 |
8201246 | Wu et al. | Jun 2012 | B1 |
8204984 | Aziz et al. | Jun 2012 | B1 |
8214905 | Doukhvalov et al. | Jul 2012 | B1 |
8220055 | Kennedy | Jul 2012 | B1 |
8225288 | Miller et al. | Jul 2012 | B2 |
8225373 | Kraemer | Jul 2012 | B2 |
8233882 | Rogel | Jul 2012 | B2 |
8234640 | Fitzgerald et al. | Jul 2012 | B1 |
8234709 | Viljoen et al. | Jul 2012 | B2 |
8239944 | Nachenberg et al. | Aug 2012 | B1 |
8260914 | Ranjan | Sep 2012 | B1 |
8266091 | Gubin et al. | Sep 2012 | B1 |
8286251 | Eker et al. | Oct 2012 | B2 |
8291499 | Aziz et al. | Oct 2012 | B2 |
8307435 | Mann et al. | Nov 2012 | B1 |
8307443 | Wang et al. | Nov 2012 | B2 |
8312545 | Tuvell et al. | Nov 2012 | B2 |
8321936 | Green et al. | Nov 2012 | B1 |
8321941 | Tuvell et al. | Nov 2012 | B2 |
8332571 | Edwards, Sr. | Dec 2012 | B1 |
8365286 | Poston | Jan 2013 | B2 |
8365297 | Parshin et al. | Jan 2013 | B1 |
8370938 | Daswani et al. | Feb 2013 | B1 |
8370939 | Zaitsev et al. | Feb 2013 | B2 |
8375444 | Aziz et al. | Feb 2013 | B2 |
8381299 | Stolfo et al. | Feb 2013 | B2 |
8402529 | Green et al. | Mar 2013 | B1 |
8464340 | Ahn et al. | Jun 2013 | B2 |
8479174 | Chiriac | Jul 2013 | B2 |
8479276 | Vaystikh et al. | Jul 2013 | B1 |
8479291 | Bodke | Jul 2013 | B1 |
8510827 | Leake et al. | Aug 2013 | B1 |
8510828 | Guo et al. | Aug 2013 | B1 |
8510842 | Amit et al. | Aug 2013 | B2 |
8516478 | Edwards et al. | Aug 2013 | B1 |
8516590 | Ranadive et al. | Aug 2013 | B1 |
8516593 | Aziz | Aug 2013 | B2 |
8522348 | Chen et al. | Aug 2013 | B2 |
8528086 | Aziz | Sep 2013 | B1 |
8533824 | Hutton et al. | Sep 2013 | B2 |
8539582 | Aziz et al. | Sep 2013 | B1 |
8549638 | Aziz | Oct 2013 | B2 |
8555391 | Demir et al. | Oct 2013 | B1 |
8561177 | Aziz et al. | Oct 2013 | B1 |
8566476 | Shiffer et al. | Oct 2013 | B2 |
8566946 | Aziz et al. | Oct 2013 | B1 |
8584094 | Dadhia et al. | Nov 2013 | B2 |
8584234 | Sobel et al. | Nov 2013 | B1 |
8584239 | Aziz et al. | Nov 2013 | B2 |
8595834 | Xie et al. | Nov 2013 | B2 |
8627476 | Satish et al. | Jan 2014 | B1 |
8635696 | Aziz | Jan 2014 | B1 |
8682054 | Xue et al. | Mar 2014 | B2 |
8682812 | Ranjan | Mar 2014 | B1 |
8689333 | Aziz | Apr 2014 | B2 |
8695096 | Zhang | Apr 2014 | B1 |
8713631 | Pavlyushchik | Apr 2014 | B1 |
8713681 | Silberman et al. | Apr 2014 | B2 |
8726392 | McCorkendale et al. | May 2014 | B1 |
8739280 | Chess et al. | May 2014 | B2 |
8776229 | Aziz | Jul 2014 | B1 |
8782792 | Bodke | Jul 2014 | B1 |
8789172 | Stolfo et al. | Jul 2014 | B2 |
8789178 | Kejriwal et al. | Jul 2014 | B2 |
8793278 | Frazier et al. | Jul 2014 | B2 |
8793787 | Ismael et al. | Jul 2014 | B2 |
8805947 | Kuzkin et al. | Aug 2014 | B1 |
8806647 | Daswani et al. | Aug 2014 | B1 |
8832829 | Manni | Sep 2014 | B2 |
8850570 | Ramzan | Sep 2014 | B1 |
8850571 | Staniford et al. | Sep 2014 | B2 |
8881234 | Narasimhan et al. | Nov 2014 | B2 |
8881271 | Butler, II | Nov 2014 | B2 |
8881282 | Aziz et al. | Nov 2014 | B1 |
8898788 | Aziz et al. | Nov 2014 | B1 |
8935779 | Manni et al. | Jan 2015 | B2 |
8949257 | Shiffer et al. | Feb 2015 | B2 |
8984638 | Aziz et al. | Mar 2015 | B1 |
8990939 | Staniford et al. | Mar 2015 | B2 |
8990944 | Singh et al. | Mar 2015 | B1 |
8997219 | Staniford et al. | Mar 2015 | B2 |
9009822 | Ismael et al. | Apr 2015 | B1 |
9009823 | Ismael et al. | Apr 2015 | B1 |
9027135 | Aziz | May 2015 | B1 |
9071638 | Aziz et al. | Jun 2015 | B1 |
9104867 | Thioux et al. | Aug 2015 | B1 |
9106630 | Frazier et al. | Aug 2015 | B2 |
9106694 | Aziz et al. | Aug 2015 | B2 |
9118715 | Staniford et al. | Aug 2015 | B2 |
9159035 | Ismael et al. | Oct 2015 | B1 |
9171160 | Vincent et al. | Oct 2015 | B2 |
9176843 | Ismael et al. | Nov 2015 | B1 |
9189627 | Islam | Nov 2015 | B1 |
9195829 | Goradia et al. | Nov 2015 | B1 |
9197664 | Aziz et al. | Nov 2015 | B1 |
9223972 | Vincent et al. | Dec 2015 | B1 |
9225740 | Ismael et al. | Dec 2015 | B1 |
9241010 | Bennett et al. | Jan 2016 | B1 |
9251343 | Vincent et al. | Feb 2016 | B1 |
9262635 | Paithane et al. | Feb 2016 | B2 |
9268936 | Butler | Feb 2016 | B2 |
9275229 | LeMasters | Mar 2016 | B2 |
9282109 | Aziz et al. | Mar 2016 | B1 |
9292686 | Ismael et al. | Mar 2016 | B2 |
9294501 | Mesdaq et al. | Mar 2016 | B2 |
9300686 | Pidathala et al. | Mar 2016 | B2 |
9306960 | Aziz | Apr 2016 | B1 |
9306974 | Aziz et al. | Apr 2016 | B1 |
9311479 | Manni et al. | Apr 2016 | B1 |
9355246 | Wan et al. | May 2016 | B1 |
9355247 | Thioux et al. | May 2016 | B1 |
9356944 | Aziz | May 2016 | B1 |
9363280 | Rivlin et al. | Jun 2016 | B1 |
9367681 | Ismael et al. | Jun 2016 | B1 |
9398028 | Karandikar et al. | Jul 2016 | B1 |
9413781 | Cunningham et al. | Aug 2016 | B2 |
9426071 | Caldejon et al. | Aug 2016 | B1 |
9430646 | Mushtaq et al. | Aug 2016 | B1 |
9432361 | Mahaffey | Aug 2016 | B2 |
9432389 | Khalid et al. | Aug 2016 | B1 |
9438613 | Paithane et al. | Sep 2016 | B1 |
9438622 | Staniford et al. | Sep 2016 | B1 |
9438623 | Thioux et al. | Sep 2016 | B1 |
9459901 | Jung et al. | Oct 2016 | B2 |
9467460 | Otvagin et al. | Oct 2016 | B1 |
9483644 | Paithane et al. | Nov 2016 | B1 |
9489516 | Lu | Nov 2016 | B1 |
9495180 | Ismael | Nov 2016 | B2 |
9497213 | Thompson et al. | Nov 2016 | B2 |
9507935 | Ismael et al. | Nov 2016 | B2 |
9516057 | Aziz | Dec 2016 | B2 |
9519782 | Aziz et al. | Dec 2016 | B2 |
9536091 | Paithane et al. | Jan 2017 | B2 |
9537972 | Edwards et al. | Jan 2017 | B1 |
9560059 | Islam | Jan 2017 | B1 |
9565202 | Kindlund et al. | Feb 2017 | B1 |
9591015 | Amin et al. | Mar 2017 | B1 |
9591020 | Aziz | Mar 2017 | B1 |
9594904 | Jain et al. | Mar 2017 | B1 |
9594905 | Ismael et al. | Mar 2017 | B1 |
9594912 | Thioux et al. | Mar 2017 | B1 |
9609007 | Rivlin et al. | Mar 2017 | B1 |
9626509 | Khalid et al. | Apr 2017 | B1 |
9628498 | Aziz et al. | Apr 2017 | B1 |
9628507 | Haq et al. | Apr 2017 | B2 |
9633134 | Ross | Apr 2017 | B2 |
9635039 | Islam et al. | Apr 2017 | B1 |
9641546 | Manni et al. | May 2017 | B1 |
9654485 | Neumann | May 2017 | B1 |
9661009 | Karandikar et al. | May 2017 | B1 |
9661018 | Aziz | May 2017 | B1 |
9674298 | Edwards et al. | Jun 2017 | B1 |
9680862 | Ismael et al. | Jun 2017 | B2 |
9690606 | Ha et al. | Jun 2017 | B1 |
9690933 | Singh et al. | Jun 2017 | B1 |
9690935 | Shifter et al. | Jun 2017 | B2 |
9690936 | Malik et al. | Jun 2017 | B1 |
9736179 | Ismael | Aug 2017 | B2 |
9740857 | Ismael et al. | Aug 2017 | B2 |
9747446 | Pidathala et al. | Aug 2017 | B1 |
9754103 | Patel et al. | Sep 2017 | B1 |
9756074 | Aziz et al. | Sep 2017 | B2 |
9773112 | Rathor et al. | Sep 2017 | B1 |
9781144 | Otvagin et al. | Oct 2017 | B1 |
9787700 | Amin | Oct 2017 | B1 |
9787706 | Otvagin et al. | Oct 2017 | B1 |
9792196 | Ismael et al. | Oct 2017 | B1 |
9824209 | Ismael et al. | Nov 2017 | B1 |
9824211 | Wilson | Nov 2017 | B2 |
9824216 | Khalid et al. | Nov 2017 | B1 |
9825976 | Gomez et al. | Nov 2017 | B1 |
9825989 | Mehra et al. | Nov 2017 | B1 |
9838408 | Karandikar et al. | Dec 2017 | B1 |
9838411 | Aziz | Dec 2017 | B1 |
9838416 | Aziz | Dec 2017 | B1 |
9838417 | Khalid et al. | Dec 2017 | B1 |
9846776 | Paithane et al. | Dec 2017 | B1 |
9876701 | Caldejon et al. | Jan 2018 | B1 |
9888016 | Amin et al. | Feb 2018 | B1 |
9888019 | Pidathala et al. | Feb 2018 | B1 |
9910988 | Vincent et al. | Mar 2018 | B1 |
9912644 | Cunningham | Mar 2018 | B2 |
9912681 | Ismael et al. | Mar 2018 | B1 |
9912684 | Aziz et al. | Mar 2018 | B1 |
9912691 | Mesdaq et al. | Mar 2018 | B2 |
9912698 | Thioux et al. | Mar 2018 | B1 |
9916440 | Paithane et al. | Mar 2018 | B1 |
9921978 | Chan et al. | Mar 2018 | B1 |
9934376 | Ismael | Apr 2018 | B1 |
9934381 | Kindlund et al. | Apr 2018 | B1 |
9946568 | Ismael et al. | Apr 2018 | B1 |
9954890 | Staniford et al. | Apr 2018 | B1 |
9973531 | Thioux | May 2018 | B1 |
10002252 | Ismael et al. | Jun 2018 | B2 |
10019338 | Goradia et al. | Jul 2018 | B1 |
10019573 | Silberman et al. | Jul 2018 | B2 |
10025691 | Ismael et al. | Jul 2018 | B1 |
10025927 | Khalid et al. | Jul 2018 | B1 |
10027689 | Rathor et al. | Jul 2018 | B1 |
10027690 | Aziz et al. | Jul 2018 | B2 |
10027696 | Rivlin et al. | Jul 2018 | B1 |
10033747 | Paithane et al. | Jul 2018 | B1 |
10033748 | Cunningham et al. | Jul 2018 | B1 |
10033753 | Islam et al. | Jul 2018 | B1 |
10033759 | Kabra et al. | Jul 2018 | B1 |
10050998 | Singh | Aug 2018 | B1 |
10068091 | Aziz et al. | Sep 2018 | B1 |
10075455 | Zafar et al. | Sep 2018 | B2 |
10083302 | Paithane et al. | Sep 2018 | B1 |
10084813 | Eyada | Sep 2018 | B2 |
10089461 | Ha et al. | Oct 2018 | B1 |
10097573 | Aziz | Oct 2018 | B1 |
10104102 | Neumann | Oct 2018 | B1 |
10108446 | Steinberg et al. | Oct 2018 | B1 |
10121000 | Rivlin et al. | Nov 2018 | B1 |
10122746 | Manni et al. | Nov 2018 | B1 |
10133863 | Bu et al. | Nov 2018 | B2 |
10133866 | Kumar et al. | Nov 2018 | B1 |
10146810 | Shiffer et al. | Dec 2018 | B2 |
10148693 | Singh et al. | Dec 2018 | B2 |
10165000 | Aziz et al. | Dec 2018 | B1 |
10169585 | Pilipenko et al. | Jan 2019 | B1 |
10176321 | Abbasi et al. | Jan 2019 | B2 |
10181029 | Ismael et al. | Jan 2019 | B1 |
10191861 | Steinberg et al. | Jan 2019 | B1 |
10192052 | Singh et al. | Jan 2019 | B1 |
10198574 | Thioux et al. | Feb 2019 | B1 |
10200384 | Mushtaq et al. | Feb 2019 | B1 |
10210329 | Malik et al. | Feb 2019 | B1 |
10216927 | Steinberg | Feb 2019 | B1 |
10218740 | Mesdaq et al. | Feb 2019 | B1 |
10242185 | Goradia | Mar 2019 | B1 |
20010005889 | Albrecht | Jun 2001 | A1 |
20010047326 | Broadbent et al. | Nov 2001 | A1 |
20020018903 | Kokubo et al. | Feb 2002 | A1 |
20020038430 | Edwards et al. | Mar 2002 | A1 |
20020073129 | Wang et al. | Jun 2002 | A1 |
20020091819 | Melchione et al. | Jul 2002 | A1 |
20020095607 | Lin-Hendel | Jul 2002 | A1 |
20020116627 | Tarbotton et al. | Aug 2002 | A1 |
20020144156 | Copeland | Oct 2002 | A1 |
20020162015 | Tang | Oct 2002 | A1 |
20020166063 | Lachman et al. | Nov 2002 | A1 |
20020169952 | DiSanto et al. | Nov 2002 | A1 |
20020184528 | Shevenell et al. | Dec 2002 | A1 |
20020188887 | Largman et al. | Dec 2002 | A1 |
20020194490 | Halperin et al. | Dec 2002 | A1 |
20030021728 | Sharpe et al. | Jan 2003 | A1 |
20030074578 | Ford et al. | Apr 2003 | A1 |
20030084318 | Schertz | May 2003 | A1 |
20030101381 | Mateev et al. | May 2003 | A1 |
20030115483 | Liang | Jun 2003 | A1 |
20030188190 | Aaron et al. | Oct 2003 | A1 |
20030191957 | Hypponen et al. | Oct 2003 | A1 |
20030200460 | Morota et al. | Oct 2003 | A1 |
20030212902 | van der Made | Nov 2003 | A1 |
20030229801 | Kouznetsov et al. | Dec 2003 | A1 |
20030237000 | Denton et al. | Dec 2003 | A1 |
20040003323 | Bennett et al. | Jan 2004 | A1 |
20040006473 | Mills et al. | Jan 2004 | A1 |
20040015712 | Szor | Jan 2004 | A1 |
20040019832 | Arnold et al. | Jan 2004 | A1 |
20040047356 | Bauer | Mar 2004 | A1 |
20040083408 | Spiegel et al. | Apr 2004 | A1 |
20040088581 | Brawn et al. | May 2004 | A1 |
20040093513 | Cantrell et al. | May 2004 | A1 |
20040111531 | Staniford et al. | Jun 2004 | A1 |
20040117478 | Triulzi et al. | Jun 2004 | A1 |
20040117624 | Brandt et al. | Jun 2004 | A1 |
20040128355 | Chao et al. | Jul 2004 | A1 |
20040165588 | Pandya | Aug 2004 | A1 |
20040236963 | Danford et al. | Nov 2004 | A1 |
20040243349 | Greifeneder et al. | Dec 2004 | A1 |
20040249911 | Alkhatib et al. | Dec 2004 | A1 |
20040255161 | Cavanaugh | Dec 2004 | A1 |
20040268147 | Wiederin et al. | Dec 2004 | A1 |
20050005159 | Oliphant | Jan 2005 | A1 |
20050021740 | Bar et al. | Jan 2005 | A1 |
20050033960 | Vialen et al. | Feb 2005 | A1 |
20050033989 | Poletto et al. | Feb 2005 | A1 |
20050050148 | Mohammadioun et al. | Mar 2005 | A1 |
20050086523 | Zimmer et al. | Apr 2005 | A1 |
20050091513 | Mitomo et al. | Apr 2005 | A1 |
20050091533 | Omote et al. | Apr 2005 | A1 |
20050091652 | Ross et al. | Apr 2005 | A1 |
20050108562 | Khazan et al. | May 2005 | A1 |
20050114663 | Cornell et al. | May 2005 | A1 |
20050125195 | Brendel | Jun 2005 | A1 |
20050149726 | Joshi et al. | Jul 2005 | A1 |
20050157662 | Bingham et al. | Jul 2005 | A1 |
20050183143 | Anderholm et al. | Aug 2005 | A1 |
20050201297 | Peikari | Sep 2005 | A1 |
20050210533 | Copeland et al. | Sep 2005 | A1 |
20050238005 | Chen et al. | Oct 2005 | A1 |
20050240781 | Gassoway | Oct 2005 | A1 |
20050262562 | Gassoway | Nov 2005 | A1 |
20050265331 | Stolfo | Dec 2005 | A1 |
20050283839 | Cowburn | Dec 2005 | A1 |
20060010495 | Cohen et al. | Jan 2006 | A1 |
20060015416 | Hoffman et al. | Jan 2006 | A1 |
20060015715 | Anderson | Jan 2006 | A1 |
20060015747 | Van de Ven | Jan 2006 | A1 |
20060021029 | Brickell et al. | Jan 2006 | A1 |
20060021054 | Costa et al. | Jan 2006 | A1 |
20060031476 | Mathes et al. | Feb 2006 | A1 |
20060047665 | Neil | Mar 2006 | A1 |
20060070130 | Costea et al. | Mar 2006 | A1 |
20060075496 | Carpenter et al. | Apr 2006 | A1 |
20060095968 | Portolani et al. | May 2006 | A1 |
20060101516 | Sudaharan et al. | May 2006 | A1 |
20060101517 | Banzhof et al. | May 2006 | A1 |
20060117385 | Mester et al. | Jun 2006 | A1 |
20060123477 | Raghavan et al. | Jun 2006 | A1 |
20060143709 | Brooks et al. | Jun 2006 | A1 |
20060150249 | Gassen et al. | Jul 2006 | A1 |
20060161983 | Cothrell et al. | Jul 2006 | A1 |
20060161987 | Levy-Yurista | Jul 2006 | A1 |
20060161989 | Reshef et al. | Jul 2006 | A1 |
20060164199 | Glide et al. | Jul 2006 | A1 |
20060173992 | Weber et al. | Aug 2006 | A1 |
20060179147 | Tran et al. | Aug 2006 | A1 |
20060184632 | Marino et al. | Aug 2006 | A1 |
20060191010 | Benjamin | Aug 2006 | A1 |
20060221956 | Narayan et al. | Oct 2006 | A1 |
20060236393 | Kramer et al. | Oct 2006 | A1 |
20060242709 | Seinfeld et al. | Oct 2006 | A1 |
20060248519 | Jaeger et al. | Nov 2006 | A1 |
20060248582 | Panjwani et al. | Nov 2006 | A1 |
20060251104 | Koga | Nov 2006 | A1 |
20060288417 | Bookbinder et al. | Dec 2006 | A1 |
20070006288 | Mayfield et al. | Jan 2007 | A1 |
20070006313 | Porras et al. | Jan 2007 | A1 |
20070011174 | Takaragi et al. | Jan 2007 | A1 |
20070016951 | Piccard et al. | Jan 2007 | A1 |
20070019286 | Kikuchi | Jan 2007 | A1 |
20070033645 | Jones | Feb 2007 | A1 |
20070038943 | FitzGerald et al. | Feb 2007 | A1 |
20070064689 | Shin et al. | Mar 2007 | A1 |
20070074169 | Chess et al. | Mar 2007 | A1 |
20070094730 | Bhikkaji et al. | Apr 2007 | A1 |
20070101435 | Konanka et al. | May 2007 | A1 |
20070128855 | Cho et al. | Jun 2007 | A1 |
20070142030 | Sinha et al. | Jun 2007 | A1 |
20070143827 | Nicodemus et al. | Jun 2007 | A1 |
20070156895 | Vuong | Jul 2007 | A1 |
20070157180 | Tillmann et al. | Jul 2007 | A1 |
20070157306 | Elrod et al. | Jul 2007 | A1 |
20070168988 | Eisner et al. | Jul 2007 | A1 |
20070171824 | Ruello et al. | Jul 2007 | A1 |
20070174915 | Gribble et al. | Jul 2007 | A1 |
20070192500 | Lum | Aug 2007 | A1 |
20070192858 | Lum | Aug 2007 | A1 |
20070198275 | Malden et al. | Aug 2007 | A1 |
20070208822 | Wang et al. | Sep 2007 | A1 |
20070220607 | Sprosts et al. | Sep 2007 | A1 |
20070240218 | Tuvell et al. | Oct 2007 | A1 |
20070240219 | Tuvell et al. | Oct 2007 | A1 |
20070240220 | Tuvell et al. | Oct 2007 | A1 |
20070240222 | Tuvell et al. | Oct 2007 | A1 |
20070250930 | Aziz et al. | Oct 2007 | A1 |
20070256132 | Oliphant | Nov 2007 | A2 |
20070271446 | Nakamura | Nov 2007 | A1 |
20070283192 | Shevchenko | Dec 2007 | A1 |
20080005782 | Aziz | Jan 2008 | A1 |
20080016339 | Shukla | Jan 2008 | A1 |
20080018122 | Zierler et al. | Jan 2008 | A1 |
20080028463 | Dagon et al. | Jan 2008 | A1 |
20080040710 | Chiriac | Feb 2008 | A1 |
20080046781 | Childs et al. | Feb 2008 | A1 |
20080066179 | Liu | Mar 2008 | A1 |
20080072326 | Danford et al. | Mar 2008 | A1 |
20080077793 | Tan et al. | Mar 2008 | A1 |
20080080518 | Hoeflin et al. | Apr 2008 | A1 |
20080086720 | Lekel | Apr 2008 | A1 |
20080098476 | Syversen | Apr 2008 | A1 |
20080120722 | Sima et al. | May 2008 | A1 |
20080134178 | Fitzgerald et al. | Jun 2008 | A1 |
20080134334 | Kim et al. | Jun 2008 | A1 |
20080141376 | Clausen et al. | Jun 2008 | A1 |
20080184367 | McMillan et al. | Jul 2008 | A1 |
20080184373 | Traut et al. | Jul 2008 | A1 |
20080189787 | Arnold et al. | Aug 2008 | A1 |
20080201778 | Guo et al. | Aug 2008 | A1 |
20080209557 | Herley et al. | Aug 2008 | A1 |
20080215742 | Goldszmidt et al. | Sep 2008 | A1 |
20080222729 | Chen et al. | Sep 2008 | A1 |
20080235273 | Shipilevsky | Sep 2008 | A1 |
20080263665 | Ma et al. | Oct 2008 | A1 |
20080295172 | Bohacek | Nov 2008 | A1 |
20080301810 | Lehane et al. | Dec 2008 | A1 |
20080307524 | Singh et al. | Dec 2008 | A1 |
20080313738 | Enderby | Dec 2008 | A1 |
20080320594 | Jiang | Dec 2008 | A1 |
20090003317 | Kasralikar et al. | Jan 2009 | A1 |
20090007100 | Field et al. | Jan 2009 | A1 |
20090013408 | Schipka | Jan 2009 | A1 |
20090031423 | Liu et al. | Jan 2009 | A1 |
20090036111 | Danford et al. | Feb 2009 | A1 |
20090037835 | Goldman | Feb 2009 | A1 |
20090044024 | Oberheide et al. | Feb 2009 | A1 |
20090044274 | Budko et al. | Feb 2009 | A1 |
20090064332 | Porras et al. | Mar 2009 | A1 |
20090077666 | Chen et al. | Mar 2009 | A1 |
20090083369 | Marmor | Mar 2009 | A1 |
20090083855 | Apap et al. | Mar 2009 | A1 |
20090089879 | Wang et al. | Apr 2009 | A1 |
20090094697 | Provos et al. | Apr 2009 | A1 |
20090113425 | Ports et al. | Apr 2009 | A1 |
20090125976 | Wassermann et al. | May 2009 | A1 |
20090126015 | Monastyrsky et al. | May 2009 | A1 |
20090126016 | Sobko et al. | May 2009 | A1 |
20090133125 | Choi et al. | May 2009 | A1 |
20090144823 | Lamastra et al. | Jun 2009 | A1 |
20090158430 | Borders | Jun 2009 | A1 |
20090172815 | Gu et al. | Jul 2009 | A1 |
20090187992 | Poston | Jul 2009 | A1 |
20090193293 | Stolfo et al. | Jul 2009 | A1 |
20090198651 | Shiffer et al. | Aug 2009 | A1 |
20090198670 | Shiffer et al. | Aug 2009 | A1 |
20090198689 | Frazier et al. | Aug 2009 | A1 |
20090199274 | Frazier et al. | Aug 2009 | A1 |
20090199296 | Xie et al. | Aug 2009 | A1 |
20090228233 | Anderson et al. | Sep 2009 | A1 |
20090241187 | Troyansky | Sep 2009 | A1 |
20090241190 | Todd et al. | Sep 2009 | A1 |
20090265692 | Godefroid et al. | Oct 2009 | A1 |
20090271867 | Zhang | Oct 2009 | A1 |
20090300415 | Zhang et al. | Dec 2009 | A1 |
20090300761 | Park et al. | Dec 2009 | A1 |
20090328185 | Berg et al. | Dec 2009 | A1 |
20090328221 | Blumfield et al. | Dec 2009 | A1 |
20100005146 | Drako et al. | Jan 2010 | A1 |
20100011205 | McKenna | Jan 2010 | A1 |
20100017546 | Poo et al. | Jan 2010 | A1 |
20100030996 | Butler, II | Feb 2010 | A1 |
20100031353 | Thomas et al. | Feb 2010 | A1 |
20100037314 | Perdisci et al. | Feb 2010 | A1 |
20100043073 | Kuwamura | Feb 2010 | A1 |
20100054278 | Stolfo et al. | Mar 2010 | A1 |
20100058474 | Hicks | Mar 2010 | A1 |
20100064044 | Nonoyama | Mar 2010 | A1 |
20100077481 | Polyakov et al. | Mar 2010 | A1 |
20100083376 | Pereira et al. | Apr 2010 | A1 |
20100115621 | Staniford et al. | May 2010 | A1 |
20100132038 | Zaitsev | May 2010 | A1 |
20100154056 | Smith et al. | Jun 2010 | A1 |
20100180344 | Malyshev et al. | Jul 2010 | A1 |
20100192223 | Ismael et al. | Jul 2010 | A1 |
20100220863 | Dupaquis et al. | Sep 2010 | A1 |
20100235831 | Dittmer | Sep 2010 | A1 |
20100251104 | Massand | Sep 2010 | A1 |
20100281102 | Chinta et al. | Nov 2010 | A1 |
20100281541 | Stolfo et al. | Nov 2010 | A1 |
20100281542 | Stolfo et al. | Nov 2010 | A1 |
20100287260 | Peterson et al. | Nov 2010 | A1 |
20100287620 | Fanton | Nov 2010 | A1 |
20100299754 | Amit et al. | Nov 2010 | A1 |
20100306173 | Frank | Dec 2010 | A1 |
20110004737 | Greenebaum | Jan 2011 | A1 |
20110025504 | Lyon | Feb 2011 | A1 |
20110041179 | St Hlberg | Feb 2011 | A1 |
20110047594 | Mahaffey et al. | Feb 2011 | A1 |
20110047620 | Mahaffey et al. | Feb 2011 | A1 |
20110055907 | Narasimhan et al. | Mar 2011 | A1 |
20110078794 | Manni et al. | Mar 2011 | A1 |
20110093951 | Aziz | Apr 2011 | A1 |
20110099620 | Stavrou et al. | Apr 2011 | A1 |
20110099633 | Aziz | Apr 2011 | A1 |
20110099635 | Silberman et al. | Apr 2011 | A1 |
20110113231 | Kaminsky | May 2011 | A1 |
20110145918 | Jung et al. | Jun 2011 | A1 |
20110145920 | Mahaffey et al. | Jun 2011 | A1 |
20110145934 | Abramovici et al. | Jun 2011 | A1 |
20110167493 | Song et al. | Jul 2011 | A1 |
20110167494 | Bowen et al. | Jul 2011 | A1 |
20110173213 | Frazier et al. | Jul 2011 | A1 |
20110173460 | Ito et al. | Jul 2011 | A1 |
20110219449 | St. Neitzel et al. | Sep 2011 | A1 |
20110219450 | McDougal et al. | Sep 2011 | A1 |
20110225624 | Sawhney et al. | Sep 2011 | A1 |
20110225655 | Niemela et al. | Sep 2011 | A1 |
20110247072 | Staniford et al. | Oct 2011 | A1 |
20110265182 | Peinado et al. | Oct 2011 | A1 |
20110289582 | Kejriwal et al. | Nov 2011 | A1 |
20110302587 | Nishikawa et al. | Dec 2011 | A1 |
20110307954 | Melnik et al. | Dec 2011 | A1 |
20110307955 | Kaplan et al. | Dec 2011 | A1 |
20110307956 | Yermakov et al. | Dec 2011 | A1 |
20110314546 | Aziz et al. | Dec 2011 | A1 |
20120023593 | Puder et al. | Jan 2012 | A1 |
20120054869 | Yen et al. | Mar 2012 | A1 |
20120060220 | Kerseboom et al. | Mar 2012 | A1 |
20120066698 | Yanoo | Mar 2012 | A1 |
20120079596 | Thomas et al. | Mar 2012 | A1 |
20120084859 | Radinsky et al. | Apr 2012 | A1 |
20120096553 | Srivastava et al. | Apr 2012 | A1 |
20120110667 | Zubrilin et al. | May 2012 | A1 |
20120117652 | Manni et al. | May 2012 | A1 |
20120121154 | Xue et al. | May 2012 | A1 |
20120124426 | Maybee et al. | May 2012 | A1 |
20120174186 | Aziz et al. | Jul 2012 | A1 |
20120174196 | Bhogavilli et al. | Jul 2012 | A1 |
20120174218 | McCoy et al. | Jul 2012 | A1 |
20120198279 | Schroeder | Aug 2012 | A1 |
20120210423 | Friedrichs et al. | Aug 2012 | A1 |
20120222121 | Staniford et al. | Aug 2012 | A1 |
20120255015 | Sahita et al. | Oct 2012 | A1 |
20120255017 | Sallam | Oct 2012 | A1 |
20120260342 | Dube et al. | Oct 2012 | A1 |
20120266244 | Green et al. | Oct 2012 | A1 |
20120278886 | Luna | Nov 2012 | A1 |
20120297489 | Dequevy | Nov 2012 | A1 |
20120330801 | McDougal et al. | Dec 2012 | A1 |
20120331553 | Aziz et al. | Dec 2012 | A1 |
20130014259 | Gable et al. | Jan 2013 | A1 |
20130036472 | Aziz | Feb 2013 | A1 |
20130047257 | Aziz | Feb 2013 | A1 |
20130074185 | McDougal et al. | Mar 2013 | A1 |
20130086684 | Mohler | Apr 2013 | A1 |
20130097699 | Balupari et al. | Apr 2013 | A1 |
20130097706 | Titonis et al. | Apr 2013 | A1 |
20130111587 | Goel et al. | May 2013 | A1 |
20130117849 | Golshan et al. | May 2013 | A1 |
20130117852 | Stute | May 2013 | A1 |
20130117855 | Kim et al. | May 2013 | A1 |
20130139264 | Brinkley et al. | May 2013 | A1 |
20130160125 | Likhachev et al. | Jun 2013 | A1 |
20130160127 | Jeong et al. | Jun 2013 | A1 |
20130160130 | Mendelev et al. | Jun 2013 | A1 |
20130160131 | Madou et al. | Jun 2013 | A1 |
20130167236 | Sick | Jun 2013 | A1 |
20130174214 | Duncan | Jul 2013 | A1 |
20130185789 | Hagiwara et al. | Jul 2013 | A1 |
20130185795 | Winn et al. | Jul 2013 | A1 |
20130185798 | Saunders et al. | Jul 2013 | A1 |
20130191915 | Antonakakis et al. | Jul 2013 | A1 |
20130196649 | Paddon et al. | Aug 2013 | A1 |
20130227691 | Aziz et al. | Aug 2013 | A1 |
20130246370 | Bartram et al. | Sep 2013 | A1 |
20130247186 | LeMasters | Sep 2013 | A1 |
20130263260 | Mahaffey et al. | Oct 2013 | A1 |
20130291109 | Staniford et al. | Oct 2013 | A1 |
20130298243 | Kumar et al. | Nov 2013 | A1 |
20130318038 | Shiffer et al. | Nov 2013 | A1 |
20130318073 | Shiffer et al. | Nov 2013 | A1 |
20130325791 | Shiffer et al. | Dec 2013 | A1 |
20130325792 | Shiffer et al. | Dec 2013 | A1 |
20130325871 | Shiffer et al. | Dec 2013 | A1 |
20130325872 | Shiffer et al. | Dec 2013 | A1 |
20140032875 | Butler | Jan 2014 | A1 |
20140053260 | Gupta et al. | Feb 2014 | A1 |
20140053261 | Gupta et al. | Feb 2014 | A1 |
20140130158 | Wang et al. | May 2014 | A1 |
20140137180 | Lukacs et al. | May 2014 | A1 |
20140169762 | Ryu | Jun 2014 | A1 |
20140179360 | Jackson et al. | Jun 2014 | A1 |
20140181131 | Ross | Jun 2014 | A1 |
20140189687 | Jung et al. | Jul 2014 | A1 |
20140189866 | Shiffer et al. | Jul 2014 | A1 |
20140189882 | Jung et al. | Jul 2014 | A1 |
20140237600 | Silberman et al. | Aug 2014 | A1 |
20140280245 | Wilson | Sep 2014 | A1 |
20140283037 | Sikorski et al. | Sep 2014 | A1 |
20140283063 | Thompson et al. | Sep 2014 | A1 |
20140328204 | Klotsche et al. | Nov 2014 | A1 |
20140337836 | Ismael | Nov 2014 | A1 |
20140344926 | Cunningham et al. | Nov 2014 | A1 |
20140351935 | Shao et al. | Nov 2014 | A1 |
20140380473 | Bu et al. | Dec 2014 | A1 |
20140380474 | Paithane | Dec 2014 | A1 |
20150007312 | Pidathala et al. | Jan 2015 | A1 |
20150096022 | Vincent et al. | Apr 2015 | A1 |
20150096023 | Mesdaq et al. | Apr 2015 | A1 |
20150096024 | Haq et al. | Apr 2015 | A1 |
20150096025 | Ismael | Apr 2015 | A1 |
20150180886 | Staniford et al. | Jun 2015 | A1 |
20150186645 | Aziz et al. | Jul 2015 | A1 |
20150199513 | Ismael et al. | Jul 2015 | A1 |
20150199531 | Ismael et al. | Jul 2015 | A1 |
20150199532 | Ismael et al. | Jul 2015 | A1 |
20150205962 | Swidowski | Jul 2015 | A1 |
20150220735 | Paithane et al. | Aug 2015 | A1 |
20150372980 | Eyada | Dec 2015 | A1 |
20160004869 | Ismael et al. | Jan 2016 | A1 |
20160006756 | Ismael et al. | Jan 2016 | A1 |
20160044000 | Cunningham | Feb 2016 | A1 |
20160099963 | Mahaffey | Apr 2016 | A1 |
20160127393 | Aziz et al. | May 2016 | A1 |
20160191547 | Zafar et al. | Jun 2016 | A1 |
20160191550 | Ismael et al. | Jun 2016 | A1 |
20160261612 | Mesdaq et al. | Sep 2016 | A1 |
20160285914 | Singh et al. | Sep 2016 | A1 |
20160301703 | Aziz | Oct 2016 | A1 |
20160335110 | Paithane | Nov 2016 | A1 |
20170083703 | Abbasi et al. | Mar 2017 | A1 |
20180013770 | Ismael | Jan 2018 | A1 |
20180048660 | Paithane et al. | Feb 2018 | A1 |
20180121316 | Ismael et al. | May 2018 | A1 |
20180288077 | Siddiqui et al. | Oct 2018 | A1 |
Number | Date | Country |
---|---|---|
2439806 | Jan 2008 | GB |
2490431 | Oct 2012 | GB |
02006928 | Jan 2002 | WO |
0223805 | Mar 2002 | WO |
2007117636 | Oct 2007 | WO |
2008041950 | Apr 2008 | WO |
2011084431 | Jul 2011 | WO |
2011112348 | Sep 2011 | WO |
2012075336 | Jun 2012 | WO |
2012145066 | Oct 2012 | WO |
2013067505 | May 2013 | WO |
2014147618 | Sep 2014 | WO |
Entry |
---|
“Mining Specification of Malicious Behavior”—Jha et al, UCSB, Sep. 2007 https://www.cs.ucsb.edu/.about.chris/research/doc/esec07.sub.--mining.pdf-. |
“Network Security: NetDetector—Network Intrusion Forensic System (NIFS) Whitepaper”, (“NetDetector Whitepaper”), (2003). |
“When Virtual is Better Than Real”, IEEEXplore Digital Library, available at, http://ieeexplore.ieee.org/xpl/articleDetails.isp?reload=true&arnumbe- r=990073, (Dec. 7, 2013). |
Abdullah, et al., Visualizing Network Data for Intrusion Detection, 2005 IEEE Workshop on Information Assurance and Security, pp. 100-108. |
Adetoye, Adedayo , et al., “Network Intrusion Detection & Response System”, (“Adetoye”), (Sep. 2003). |
Apostolopoulos, George; hassapis, Constantinos; “V-eM: A cluster of Virtual Machines for Robust, Detailed, and High-Performance Network Emulation”, 14th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Sep. 11-14, 2006, pp. 117-126. |
Aura, Tuomas, “Scanning electronic documents for personally identifiable information”, Proceedings of the 5th ACM workshop on Privacy in electronic society. ACM, 2006. |
Baecher, “The Nepenthes Platform: An Efficient Approach to collect Malware”, Springer-verlag Berlin Heidelberg, (2006), pp. 165-184. |
Bayer, et al., “Dynamic Analysis of Malicious Code”, J Comput Virol, Springer-Verlag, France., (2006), pp. 67-77. |
Boubalos, Chris , “extracting syslog data out of raw pcap dumps, seclists.org, Honeypots mailing list archives”, available at http://seclists.org/honeypots/2003/q2/319 (“Boubalos”), (Jun. 5, 2003). |
C. Kolbitsch et al., “The Power of Procrastination: Detection and Mitigation of Execution-Stalling Malicious Code” in Proceedings of the 18th ACM Conference on Computer and Communications Security, 2011, ACM, pp. 1-12. |
Chaudet, C. , et al., “Optimal Positioning of Active and Passive Monitoring Devices”, International Conference on Emerging Networking Experiments and Technologies, Proceedings of the 2005 ACM Conference on Emerging Network Experiment and Technology, CoNEXT '05, Toulousse, France, (Oct. 2005), pp. 71-82. |
Chen, P. M. and Noble, B. D., “When Virtual is Better Than Real, Department of Electrical Engineering and Computer Science”, University of Michigan (“Chen”) (2001). |
Cisco “Intrusion Prevention for the Cisco ASA 5500-x Series” Data Sheet (2012). |
Cohen, M.I. , “PyFlag—an advanced network forensic framework”, Digital investigation 5, Elsevier, (2008), pp. S112-S120. |
Costa, M. , et al., “Vigilante: End-to-End Containment of Internet Worms”, SOSP '05, Association for Computing Machinery, Inc., Brighton U.K., (Oct. 23-26, 2005). |
Didier Stevens, “Malicious PDF Documents Explained”, Security & Privacy, IEEE, IEEE Service Center, Los Alamitos, CA, US, vol. 9, No. 1, Jan. 1, 2011, pp. 80-82, XP011329453, ISSN: 1540-7993, DOI: 10.1109/MSP.2011.14. |
Distler, “Malware Analysis: An Introduction”, SANS Institute InfoSec Reading Room, SANS Institute, (2007). |
Dunlap, George W. , et al., “ReVirt: Enabling Intrusion Analysis through Virtual-Machine Logging and Replay”, Proceeding of the 5th Symposium on Operating Systems Design and Implementation, USENIX Association, “Dunlap”), (Dec. 9, 2002). |
FireEye Malware Analysis & Exchange Network, Malware Protection System, FireEye Inc., 2010. |
FireEye Malware Analysis, Modern Malware Forensics, FireEye Inc., 2010. |
FireEye v.6.0 Security Target, pp. 1-35, Version 1.1, FireEye Inc., May 2011. |
Goel, et al., Reconstructing System State for Intrusion Analysis, Apr. 2008 SIGOPS Operating Systems Review, vol. 42 Issue 3, pp. 21-28. |
Gregg Keizer: “Microsoft's HoneyMonkeys Show Patching Windows Works”, Aug. 8, 2005, XP055143386, Retrieved from the Internet: URL:http://www.informationweek.com/microsofts-honeymonkeys-show-patching-windows-works/d/d-d/1035069? [retrieved on Jun. 1, 2016]. |
Heng Yin et al, Panorama: Capturing System-Wide Information Flow for Malware Detection and Analysis, Research Showcase @ CMU, Carnegie Mellon University, 2007. |
Hiroshi Shinotsuka, Malware Authors Using New Techniques to Evade Automated Threat Analysis Systems, Oct. 26, 2012, http://www.symantec.com/connect/blogs/, pp. 1-4. |
Idika et al., A-Survey-of-Malware-Detection-Techniques, Feb. 2, 2007, Department of Computer Science, Purdue University. |
Isohara, Takamasa, Keisuke Takemori, and Ayumu Kubota. “Kernel-based behavior analysis for android malware detection.” Computational intelligence and Security (CIS), 2011 Seventh International Conference on. IEEE, 2011. |
J. Crandall et al., “Temporal Search: Detecting Hidden Timebombs with Virtual Machines” in Proceedings of 9th International Conference on Architectural Support for Programming Languages and Operating Systems, 2006, ACM, pp. 25-36. |
Kaeo, Merike , “Designing Network Security”, (“Kaeo”), (Nov. 2003). |
Kevin A Roundy et al: “Hybrid Analysis and Control of Malware”, Sep. 15, 2010, Recent Advances in Intrusion Detection, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 317-338, XP019150454 ISBN:978-3-642-15511-6. |
Khaled Salah et al: “Using Cloud Computing to Implement a Security Overlay Network”, Security & Privacy, IEEE, IEEE Service Center, Los Alamitos, CA, US, vol. 11, No. 1, Jan. 1, 2013 (Jan. 1, 2013). |
Kim, H. , et al., “Autograph: Toward Automated, Distributed Worm Signature Detection”, Proceedings of the 13th Usenix Security Symposium (Security 2004), San Diego, (Aug. 2004), pp. 271-286. |
King, Samuel T., et al., “Operating System Support for Virtual Machines”, (“King”), (2003). |
Kreibich, C. , et al., “Honeycomb-Creating Intrusion Detection Signatures Using Honeypots”, 2nd Workshop on Hot Topics in Networks (HotNets-11), Boston, USA, (2003). |
Kristoff, J. , “Botnets, Detection and Mitigation: DNS-Based Techniques”, NU Security Day, (2005), 23 pages. |
Lastline Labs, The Threat of Evasive Malware, Feb. 25, 2013, Lastline Labs, pp. 1-8. |
Li et al., A VMM-Based System Call Interposition Framework for Program Monitoring, Dec. 2010, IEEE 16th International Conference on Parallel and Distributed Systems, pp. 706-711. |
Lindorfer, Martina, Clemens Kolbitsch, and Paolo Milani Comparetti. “Detecting environment-sensitive malware.” Recent Advances in Intrusion Detection. Springer Berlin Heidelberg, 2011. |
Marchette, David J., “Computer Intrusion Detection and Network Monitoring: A Statistical Viewpoint”, (“Marchette”), (2001). |
Moore, D. , et al., “Internet Quarantine: Requirements for Containing Self-Propagating Code”, INFOCOM, vol. 3, (Mar. 30-Apr. 3, 2003), pp. 1901-1910. |
Morales, Jose A., et al., ““Analyzing and exploiting network behaviors of malware.””, Security and Privacy in Communication Networks. Springer Berlin Heidelberg, 2010. 20-34. |
Mori, Detecting Unknown Computer Viruses, 2004, Springer-Verlag Berlin Heidelberg. |
Natvig, Kurt , “SANDBOXII: Internet”, Virus Bulletin Conference, (“Natvig”), (Sep. 2002). |
NetBIOS Working Group. Protocol Standard for a NetBIOS Service on a TCP/UDP transport: Concepts and Methods. STD 19, RFC 1001, Mar. 1987. |
Newsome, J. , et al., “Dynamic Taint Analysis for Automatic Detection, Analysis, and Signature Generation of Exploits on Commodity Software”, In Proceedings of the 12th Annual Network and Distributed System Security, Symposium (NDSS '05), (Feb. 2005). |
Nojiri, D. , et al., “Cooperation Response Strategies for Large Scale Attack Mitigation”, DARPA Information Survivability Conference and Exposition, vol. 1, (Apr. 22-24, 2003), pp. 293-302. |
Oberheide et al., CloudAV.sub.--N-Version Antivirus in the Network Cloud, 17th USENIX Security Symposium USENIX Security '08 Jul. 28-Aug. 1, 2008 San Jose, CA. |
Reiner Sailer, Enriquillo Valdez, Trent Jaeger, Roonald Perez, Leendert van Doorn, John Linwood Griffin, Stefan Berger., sHype: Secure Hypervisor Appraoch to Trusted Virtualized Systems (Feb. 2, 2005) (“Sailer”). |
Silicon Defense, “Worm Containment in the Internal Network”, (Mar. 2003), pp. 1-25. |
Singh, S. , et al., “Automated Worm Fingerprinting”, Proceedings of the ACM/USENIX Symposium on Operating System Design and Implementation, San Francisco, California, (Dec. 2004). |
Thomas H. Ptacek, and Timothy N. Newsham , “Insertion, Evasion, and Denial of Service: Eluding Network Intrusion Detection”, Secure Networks, (“Ptacek”), (Jan. 1998). |
Towards Understanding Malware Behaviour by the Extraction of API Calls, Alazab, M.; Venkataraman, S.; Watters, P. Cybercrime and Trustworthy Computing Workshop (CTC), 2010 Second Year: 2010 pp. 52-59, DOI: 10.1109/CTC.2010.8. |
Venezia, Paul , “NetDetector Captures Intrusions”, InfoWorld Issue 27, (“Venezia”), (Jul. 14, 2003). |
Vladimir Getov: “Security as a Service in Smart Clouds—Opportunities and Concerns”, Computer Software and Applications Conference (COMPSAC), 2012 IEEE 36th Annual, IEEE, Jul. 16, 2012 (Jul. 16, 2012). |
Wahid et al., Characterising the Evolution in Scanning Activity of Suspicious Hosts, Oct. 2009, Third International Conference on Network and System Security, pp. 344-350. |
Whyte, et al., “DNS-Based Detection of Scanning Works in an Enterprise Network”, Proceedings of the 12th Annual Network and Distributed System Security Symposium, (Feb. 2005), 15 pages. |
Williamson, Matthew M., “Throttling Viruses: Restricting Propagation to Defeat Malicious Mobile Code”, ACSAC Conference, Las Vegas, NV, USA, (Dec. 2002), pp. 1-9. |
Yuhei Kawakoya et al: “Memory behavior-based automatic malware unpacking in stealth debugging environment”, Malicious and Unwanted Software (Malware), 2010 5th International Conference on, IEEE, Piscataway, NJ, USA, Oct. 19, 2010, pp. 39-46, XP031833827, ISBN:978-1-4244-8-9353-1. |
Zhang et al., The Effects of Threading, Infection Time, and Multiple-Attacker Collaboration on Malware Propagation, Sep. 2009, IEEE 28th International Symposium on Reliable Distributed Systems, pp. 73-82. |
U.S. Appl. No. 15/197,647, filed Jun. 29, 2016 Non-Final Office Action dated Jan. 21, 2020. |
U.S. Appl. No. 15/197,647, filed Jun. 29, 2016 Final Office Action dated Jul. 15, 2019. |
U.S. Appl. No. 15/197,647, filed Jun. 29, 2016 Non-Final Office Action dated Dec. 31, 2018. |
Number | Date | Country | |
---|---|---|---|
62235494 | Sep 2015 | US |