System and method for triggering analysis of an object for malware in response to modification of that object

Information

  • Patent Grant
  • 10872151
  • Patent Number
    10,872,151
  • Date Filed
    Friday, November 16, 2018
    6 years ago
  • Date Issued
    Tuesday, December 22, 2020
    4 years ago
Abstract
According to one embodiment, a system featuring one or more processors and memory that includes monitoring logic. During operation, the monitoring logic is configured to monitor for and detect a notification message that is directed to a destination other than the monitoring logic and identify an event associated with a change in state of a data store associated with the file system to occur. The notification message, at least in part, triggers a malware analysis to be conducted on an object associated with the state change event.
Description
FIELD

Embodiments of the disclosure relate to cyber security. More particularly, embodiments of the disclosure are related to a system and method for triggered analysis of an object for a presence of malware based on an event detected at a file system.


GENERAL BACKGROUND

Over the last decade, file sharing systems that are accessible over the Internet or other publicly accessible networks have been increasingly targeted for malicious attack. One type of malicious attack may involve an attempt, normally through unsuspected uploading of malicious data (e.g., software, data, command(s), etc.) within content stored within a file sharing system, to infect any or all computers that upload the content. The malicious data, generally referred to as “malware,” may allow a third party to adversely influence or attack normal operations of the computer where the malicious attack is directed to a vulnerability associated with a specific application (e.g., browser application, document reader application, data processing application, etc.).


For instance, it is recognized that the malicious data may include a program or file that is harmful by design to the computing device. The malicious data may include computer viruses, worms, or any other executable (binary) that gathers or attempts to steal information from the computer, or otherwise operates without permission. The owners of the computers are often unaware that the malicious data has been added to their computers and is in operation.


Various processes and devices have been employed to prevent malicious attacks and other security threats on a file sharing system. Previously, security appliances were placed in-line with a storage server in an attempt to detect malware, in the form of an exploit or some sort of malicious software, as it is being routed into the storage server. However, for that deployment, conventional security appliances were required to understand and process packets configured in accordance with a storage protocol supported by a file system utilized by the storage server, where file system storage protocols are highly divergent. In fact, different types of file system may support different storage protocols and even different storage protocols may be used on different versions of the same type of file system. Additionally, the conventional in-line security appliances caused latency in the retrieval of files or other documents from the storage server. This latency adversely influenced the overall user experience provided by the file sharing system.


In fact, a security appliance offered by FireEye, Inc., the assignee of the present patent application, employs a two-phase malware detection approach to analyze files stored on a file system. This security appliance typically runs an analysis by traversing a storage tree to identify files to scan, and comparing the time of the last scan with the last modification of the file to reduce overhead by limiting its analysis to avoid repeating the scans of files not modified since the prior scanning period. It is noted that the complexity of this type of security appliance greatly increases as the storage volumes increase and storage protocols utilized by the file systems change.





BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure 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:



FIG. 1 is an exemplary block diagram of a physical representation of an enterprise network including a storage server that is part of a file system in communication with a threat detection system.



FIG. 2 is an exemplary embodiment of the storage server of FIG. 1.



FIG. 3 is an exemplary block diagram of a logical representation of the storage server of FIG. 2.



FIG. 4 is an exemplary embodiment of the threat detection system that communicates with monitoring logic deployed within a file system of the storage server of FIG. 2.



FIG. 5 is an exemplary embodiment of operations conducted during the inter-connectivity between the monitoring logic and the threat detection system of FIGS. 2-4.





DETAILED DESCRIPTION

Various embodiments of the disclosure are directed to a threat detection system that takes advantage of notification messages that are issued by many types of conventional storage systems, such as file system or a database system for example, in response to a state change event. A state change event represents a detected activity that caused a change in state of a data store within the storage system. As an example, the state change event can occur in response to a requested modification of a stored object (e.g., file, document, etc.) such as a file being updated and rewritten to the data store. As another example, the state change event can occur in response to a request to store an object into the data store such as a new file being stored within the data store where subsequent retrieval and modification of the new file is controlled by the storage system. For convenience, the storage system may be described in terms of a file system within the description that follows, but the scope of the claims is not necessarily so limited.


According to one embodiment of the disclosure, monitoring logic may be configured to monitor for (sometimes referred to as “hook”) signaling issued in response by storage control logic, which may be part of the file system that controls storage and retrieval of objects within a storage server. The signaling may include a notification message that identifies an occurrence of a state change event, where the notification message occurs after completion of the change of state (e.g., file operation). However, it is contemplated that the notification message may occur prior to completion of the change of state, provided that the storage of the object is completed before malware detection analysis is conducted. For instance, the notification message may be prompted in response to receipt of a request message directed to a kernel mode of the storage server to change the state of the object (e.g., add, delete or modify the object in the data store) and/or a response message from the kernel mode of the storage system to indicate that the requested state change has been completed.


Being configured to interact with an Application Programming Interface (API) provided by the storage (file) system, the monitoring logic is able to monitor, using the API, for one or more notification messages that are responsive to particular state change events. In response to detecting a notification message, the monitoring logic extracts an identifier of the object upon which a state change event has occurred (hereinafter referred to as the “suspect object”). The identifier provides information that specifies a unique location of the suspect object within the storage system, where the monitoring logic passes the identifier of the suspect object (and/or additional data associated with the identifier) to the threat detection system. The threat detection system uses the receipt of the identifier as a trigger to obtain the suspect object from the storage (file) system and analyze the suspect object to confirm that the suspect object is free of malware.


Herein, according to one embodiment of the disclosure, the identifier of the suspect object (hereinafter “object identifier”) may include a file path (e.g., a pointer to a storage location of the suspect object within the data store of the storage server as assigned by the file system). It is contemplated that, according to this embodiment, the object identifier may be represented as a string of characters separated by a delimiting character (e.g. “/”) that represent a directory tree hierarchy as organized by the file system. According to another embodiment of the disclosure, the object identifier may include a unique name assigned to the suspect object (e.g., file name) by the file system.


Stated differently, the malware analysis conducted by the threat detection system may be triggered by receipt of a notification message in response to a state change event (e.g., adding, deleting or modifying a stored object). The notification message may be intercepted, trapped, or monitored, sometimes referred to as “hooked”, by the monitoring logic. Upon detecting the notification message (or portions thereof), the monitoring logic identifies the suspect object, namely the object being added, deleted or modified within the storage system. Such identification may be performed by the monitoring logic extracting metadata from the notification message, where the metadata may include the object identifier that identifies a location of the suspect object in the storage server. Representing a location of the suspect object, the object identifier may be in the form of a path to a storage location of the suspect object, name of the suspect object, or the like. The object identifier is provided to the threat protection system that, after receipt, may fetch the object and conduct a static analysis and/or behavioral (dynamic) analysis on the suspect object to determine whether the suspect object is malicious. This static analysis may be conducted by comparing characteristics of the suspect object to known malicious objects (black list) or known benign objects (white list) while the behavioral analysis may be conducted by processing the suspect object, accessed via the object identifier, and determining whether the processing of the suspect object causes any anomalous (unexpected) behaviors to occur.


Furthermore, the monitoring logic may provide information to the threat detection system to initiate a comprehensive alert message that notifies an administrator of the details (e.g., storage location, IP address, object name, etc.) of an object under analysis that is currently stored in the data store of the storage server. Furthermore, the monitoring logic provides a communication path between the storage system and the threat detection system so that, in response to classifying the object under analysis as malicious, the object is removed from the data store or quarantined. Additionally, or in the alternative, the storage (file) system may be configured to substitute the suspect object deemed to be malicious by the threat detection system with a placeholder object (e.g., a text file, etc.). When the placeholder object is subsequently accessed by the electronic device, the placeholder object causes the electronic device processing the placeholder object to generate a notification that warns the user of removal and/or quarantine of the subject object. The warning may include more details about the suspect object (e.g., information regarding the malware type present in the suspect object, contact information for an administrator of the storage system, etc.).


In light of the foregoing, the behavioral analysis of the suspect object is based on and responsive to the “hooked” notification message that is already being issued by the storage control logic that is part of the file system when the suspect object undergoes a change in (storage) state. Additionally, the behavioral analysis may be performed out-of-band instead of on the same path as the request message from the electronic device that is seeking access to the suspect object.


I. Terminology


In the following description, certain terminology is used to describe various features of the invention. For example, the terms “logic,” and “engine” may be representative of hardware, firmware 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 are not limited or restricted to a hardware processor (e.g., microprocessor with one or more processor cores, a digital signal processor, any type of programmable gate array, a microcontroller, an application specific integrated circuit “ASIC”, etc.), a semiconductor memory, or combinatorial elements.


Logic (or engine) may be software such as one or more processes, one or more instances, Application Programming Interface(s) (API), subroutine(s), function(s), applet(s), servlet(s), routine(s), source code, object code, shared library/dynamic link library (dll), or even one or more instructions. This software 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; non-persistent storage such as volatile memory (e.g., any type of random access memory “RAM”); or 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 logic (or engine/component) may be stored in persistent storage.


The term “object” generally relates to a collection of data, whether in transit (e.g., over a network) or at rest (e.g., stored), often having a logical structure or organization that enables the object to be classified for purposes of analysis for malware. Examples of different types of objects may include a self-contained file that is separate from or is part of a flow. A “flow” generally refers to related packets that are received, transmitted, or exchanged within a communication session. For convenience, a packet broadly refers to a series of bits or bytes having a prescribed format. The object may correspond to a non-executable or executable file. Examples of a non-executable file may include a document (e.g., a Portable Document Format “PDF” document, word processing document such as Microsoft® Office® document, Microsoft® Excel® spreadsheet, etc.), a downloaded web page, a collection of documents (e.g., a compressed file including two or more documents), or the like. An executable file may be a program that may be made available to an operating system (OS) or an application within the storage server, where an out of the program may be received by a number of electronic devices.


The term “message” generally refers to information placed in a prescribed format. Each message may be in the form of one or more packets, frames, HTTP-based transmissions, a Short Message Service (SMS) text, a Simple Mail Transfer Protocol (SMTP) transmission, or any other series of bits having the prescribed format.


The term “network device” should be generally construed as electronics with data processing capability and/or a capability of connecting to any type of network, such as a public network (e.g., Internet), a private network (e.g., a local area network “LAN”, wireless LAN, etc.), or a combination of networks. Examples of a network device may include, but are not limited or restricted to, the following: a security appliance that includes any system or subsystem configured to perform functions associated with malware detection on an incoming object; a server, a mainframe, firewall, a router; or an endpoint device (e.g., a laptop, a smartphone, a tablet, a desktop computer, a netbook, a medical device, or any general-purpose or special-purpose, user-controlled network device).


According to one embodiment, the term “malware” may be construed broadly as any code or activity that initiates a malicious attack and/or operations associated with anomalous or unwanted behavior. For instance, malware 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. Malware may also correspond to an exploit, namely information (e.g., executable code, data, command(s), etc.) that attempts to take advantage of a vulnerability in software and/or an action by a person gaining unauthorized access to one or more areas of a network device to cause the network device to experience undesirable or anomalous behaviors. The undesirable or anomalous behaviors may include a communication-based anomaly or an execution-based anomaly, which, for example, could (1) alter the functionality of a network device executing application software in an atypical manner (a file is opened by a first process where the file is configured to be opened by a second process and not the first process); (2) alter the functionality of the network device executing that application software without any malicious intent; and/or (3) provide unwanted functionality which may be generally acceptable in another context. Additionally, malware may be code that initiates unwanted behavior which may be, as one example, uploading a contact list from an endpoint device to cloud storage without receiving permission from the user.


The term “interconnect” may be construed as a physical or logical communication path between two or more network devices or between different logic (engine/components). For instance, a physical communication path may include wired or wireless transmission mediums. Examples of wired transmission mediums and wireless transmission mediums may include electrical wiring, optical fiber, cable, bus trace, a radio unit that supports radio frequency (RF) signaling, or any other wired/wireless signal transfer mechanism. A logical communication path may include an inter-process communication (IPC) mechanism or other communication mechanism that allows for signaling between different logic.


The term “computerized” generally represents that any corresponding operations are conducted by hardware in combination with software 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.


II. General System Architecture


Referring to FIG. 1, an exemplary block diagram of a physical representation of an enterprise network 100 that features a storage server 120, which provides a storage system 130 for controlling the storage and retrieval of objects (e.g., non-executable files). As shown, the enterprise network 100 comprises a network 110 including one or more interconnects that provide connectivity between the storage server 120, one or more network devices 1401-140N (N≥1), and a threat detection system 150.


Herein, the storage system 130 may include, but is not limited or restricted to a file system that allows each of the network devices 1401-140N to store and/or access one or more objects (e.g. files) stored in the storage server 120. Communicatively coupled to or integrated within a portion of the file system 130, monitoring logic 160 is configured to monitor for selected state change events, and in particular a notification message 170 associated with any of the selected state change events. The notification message 170 indicates a change in state of a suspect object 180 that is resides in or is targeted for storage in a data store 185 of the storage server 120 (e.g., modification of a stored file within the data store 185, adding a file for storage within the data store 185 of the storage server 120, deleting a file from the data store 185 of the storage server 120, etc.). According to one embodiment, the monitoring logic 160 may be a plug-in communicatively coupled to an Application Programming Interface (API) associated with the portion of the storage (file) system 130. Of course, it is contemplated that the monitoring logic 160 may be a software component that is different than a plug-in or such functionality may be integrated as part of the file system 130.


In response to detecting the notification message 170, the monitoring logic 160 routes some or all of the data within notification message 170, most notably metadata 190 for identifying a storage location of the suspect object 180 within the storage server 120, to the threat detection system 150. In response to receipt of the metadata 190 (sometimes referred to as the “object identifier” 190), the threat detection system 150 may access the suspect object 180 and conduct static and/or behavioral (dynamic) analysis on some or all of the suspect object 180 to determine whether or not the suspect object 180 has a probability of being associated with a malicious attack that exceeds a prescribed level of probability (e.g., greater than 50%, 70%, 80%, or 90%, etc.).


In the event that the suspect object 180 is determined by the threat detection system 150 to be malicious, the threat detection system 150 may initiate an alert message to an administrator as described below. Furthermore, the threat detection system 150 may return a message 195 to the monitoring logic 160 that identifies the suspect object 180 is malicious. This may cause the storage (e.g., file) system 130 to initiate an operation to remove the suspect object 180 from the data store 185 of the storage server 120 or re-locate the suspect object 180 within the data store 185. Optionally, although not shown, logic within the storage (e.g., file) system 130 may substitute the suspect object 180 with a text object (e.g., file) (not shown) operating as a placeholder. The text file may cause display of a message on a display screen of a network device (e.g., network device 1401) to identify that the suspect object 180 has been quarantined or removed, and in some cases, information that allows the entity attempting to access the suspect object 180 to contact an administrator.


Referring now to FIG. 2, an exemplary block diagram of a physical representation of a network device (e.g., storage server 120 of FIG. 1) is shown. The storage server 120 is configured with a storage (e.g., file) system 130 that controls storage and retrieval of one or more object(s) 200 (e.g., files), including the suspect object 180 of FIG. 1, within a non-transitory storage medium 220. Herein, the storage server 120 comprises one or more hardware processors (referred to as “processor(s)”) 210, the non-transitory storage medium 220, one or more network interfaces (referred to as “network interface(s)”) 230, and one or more network devices (referred to as “network device(s)”) 240 connected by a system interconnect 250, such as a bus. These components are at least partially encased in a housing 260, which is made entirely or partially of a rigid material (e.g., hardened plastic, metal, glass, composite, or any combination thereof) that protects these components from environmental conditions.


The processor(s) 210 is a multipurpose, programmable component that is configured to accept digital data as input and process the input data in accordance with stored instructions. The input data may include a storage access request message (e.g., file write request, file create request, file delete request, etc.) from an endpoint device controlled by a user. One example of a processor may include an Intel® x86 central processing unit (CPU) with an instruction set architecture. Alternatively, a processor may include another type of CPU, a digital signal processor (DSP), an Application Specific Integrated Circuit (ASIC), a field-programmable gate array (FPGA), or other logic with data processing capability. The processor(s) 210 and the file system 130 within the non-transitory storage medium 220 collectively operate as a system resource that allows for storage and subsequent retrieval of one or more objects 200, such as a non-executable file for example, remotely from the endpoint device (e.g., network device 1401 of FIG. 1).


The network device(s) 240 may include various input/output (I/O) or peripheral devices, such as a keyboard, key pad, touch screen, or mouse for example. Each network interface 230 may include one or more network ports containing the mechanical, electrical and/or signaling circuitry needed to connect the storage server 120 to network 110 of FIG. 1 (or optionally a network interface card “NIC”) thereby facilitate communications to other remotely located network devices 1401-140N, as shown in FIG. 1. Hence, the network interface(s) 230 may be configured to transmit and/or receive access request messages from network devices 1401, . . . or, 140N using a variety of communication protocols including, inter alia, Transmission Control Protocol/Internet Protocol (TCP/IP), Hypertext Transfer Protocol (HTTP), HTTP Secure (HTTPS), SMS, iMessage, or the like.


The non-transitory storage medium 220 operates as the data store. From a logical perspective, the non-transitory storage medium 220 includes a plurality of locations that are addressable by the processor(s) 210 and the network interface(s) 230 for storing logic, including monitoring logic 160 communicatively coupled to storage control logic 270 of the file system 130. As deployed, the storage control logic 270 controls the storage and retrieval of object(s) 200 from the non-transitory storage medium 220. The monitoring logic 160 is configured to monitor for signaling that identifies a state change in stored content within the non-transitory storage medium 220, where such signaling includes the notification message 170 that identifies the object that is being added to, deleted from or modified within the storage server 120.


According to one embodiment, as further shown in FIG. 2, the monitoring logic 160 may be a plug-in that is configured to detect the notification message 170 initiated by the storage control logic 270. Herein, the monitoring logic 160 may be implemented as part of the storage (file) system 130 to detect notification messages 170 that is responsive to certain types of state change events conducted on a stored object 200 that is being monitored or may be configured to detect certain selected notification messages via an Application Programming Interface (API) 280, as shown. The API 280 provides the monitoring logic 160 with accessibility to the storage control logic 270 within the kernel mode of the storage server 120 to detect certain changes to the stored object(s) that are initiated by a particular request message from any of network devices 1401-140N of FIG. 1 being monitored. For instance, the request message may include a write request message (e.g., File Write request) received by the file system 130 from network devices 1401 of FIG. 1 to update (modify and subsequent storage of) one of the stored object(s) 200. Another request message may include an object addition request message (e.g., Create File request) to add an object (e.g., a non-executable file) for storage within the data store 185 of the storage server 120.


In response to detecting a particular notification message 170 being monitored, the monitoring logic 160 extracts metadata that identifies the object undergoing a state change (herein, “object identifier”), which may identify a file currently stored on the storage server 120 that is being updated or a new file currently being written to (i.e., stored on) the storage server 120. Thereafter, the monitoring logic 160 operates in connection with the network interface(s) 230 to transmit the object identifier to the threat detection system 150 of FIG. 1, where the object identifier may be used by the threat detection system 150 to retrieve the object for conducting behavioral analysis on that object to determine whether the object is associated with a malicious attack. Alternatively, the object identifier may be provided with a copy of that object to the threat detection system 150 in lieu of the “pull” (fetch) mechanism generally described previously.


As shown in FIG. 3, an exemplary block diagram of a logical representation of the storage server 120 of FIG. 2 is shown. Herein, the storage server 120 comprises a user mode 300 and a kernel mode 330. Herein, in user mode 300, an application or other executing code is unable to directly access hardware or reference memory. Rather, the application or other executing code accesses hardware or memory via a system API. In kernel mode, however, a driver or other executing code may have complete and unrestricted access to the underlying hardware. Hence, kernel mode 330 is generally reserved for the lowest-level (highest privileged), most trusted file system functionality such as the storage control logic 270 of FIG. 2. Access by monitoring logic 160 within the user mode 300 to notification messages issued by the storage control logic 270 in response to state change events is provided through the API 280.


Herein, a request message 310 for accessing an object or request an update or storage of the object within the storage server 120 is provided from a kernel mode of the network device accessible to the user (e.g., network device 1401 of FIG. 1) to storage control logic 270 of the file system situated within the kernel mode 330 of the storage server. The storage control logic 270 performs a state change event (e.g., modifies the file through a write access), and issues the notification message 170 as a return message for the access request message. The presence of the notification message 170 may be detected by the monitoring logic 160 via the API 280. In response, the monitoring logic 160 extracts the object identifier associated with the object undergoing a state change and provides the object identifier to the threat detection system 150 of FIG. 1, which may be used by the threat detection system 150 to retrieve the object.


III. Threat Detection System Architecture


Referring to FIG. 4, an exemplary embodiment of the threat detection system 150 that communicates with monitoring logic (e.g., a plug-in) deployed within a file system of the storage server 120 is shown. The threat detection system 150 is adapted to analyze the suspect object 180 associated with file that is newly stored, deleted or stored and modified within the file system 130. According to this illustrative embodiment, the threat detection system 150 may be communicatively coupled with the network 110 via interface logic 410, where the network 110 may operate as a public network such as the Internet or a private network (e.g., a local area network “LAN”, wireless LAN, etc.). The interface logic 410 is configured to receive some or all of the data within a detected notification message, most notably the metadata 190 that identifies a storage location of the suspect object 180 within the storage server 120, which is routed to the threat detection system 150. For instance, as an illustrative example, the interface logic 410 may be a data capturing device that automatically (or on command) accesses data stored in a storage system or another type of interface, such as a port, for receiving objects manually provided via a suitable dedicated communication link or from storage media such as a solid-state drive or flash drive.


As shown in FIG. 4, the interface logic 410 operates as a data capturing device that receives incoming data 424, namely the metadata (object identifier) 190 and/or the suspect object 180. Alternatively, the interface logic 410 can be integrated into an intermediary device in the communication path (e.g., an optional firewall, router, switch or other networked electronic device) or may be deployed as a standalone component, such as an appropriate commercially available network tap, as shown.


According to one embodiment of the disclosure, the metadata 190 may be used, at least in part by interface logic 410 when the suspect object 180 is not part of the incoming data 424, to determine if the object identifier, which identifies a storage location of the suspect object 180 in the storage server 120, is provided with the metadata 190. If so, the interface logic 410 initiates communications to fetch the suspect object 180 from the storage server 120. It is contemplated that the metadata 190 may be further used to determine protocols, application types and other information, which may be used by logic within the threat detection system 200 such as a scheduler 435 or other logic such as a virtual machine monitor (not shown) for example, to determine a particular software profile used for virtual machine (VM) configuration and/or VM operation scheduling. As an example, one or more software profiles may be used for initial configuration of guest software of one or more VMs 4601-460M (M≥1) operating within dynamic analysis system 450. Fetched from a storage device 440, these software profile(s) may be directed to different types of applications (e.g., different versions of the same application type, different application types, etc.).


As further shown in FIG. 4, the threat detection system 150 includes the interface logic 410, the static analysis system 430, the scheduler 435, the storage device 440, the dynamic analysis system 450, classification engine 480, and/or reporting engine 485. Herein, according to this embodiment of the disclosure, the interface logic 410 receives data associated with the notification message, including an object identifier. In response to receipt of the object identifier, the interface logic 410 issues a request for the suspect object identified by the object identifier.


In response to receipt of the suspect object 180, the interface logic 410 may be configured to convert that object 180 into a format, if needed or as appropriate, on which scanning may be conducted by the static analysis system 430. This conversion may involve decompression of the object for example. It is contemplated that the interface logic 410 may conduct de-compilation, disassembly or other de-obfuscation activities on the captured object 424 to produce a formatted object 426. However, as shown below, the de-obfuscation and data extraction activities may be handled by logic within the static analysis system 430.


Referring still to FIG. 4, the static analysis system 430 may analyze information associated with the formatted object 426. Such analysis may include, but is not limited or restricted to, an analysis of the object type and may extract one or more characteristics (hereinafter “characteristic(s)”) associated with the formatted object 426, such as the object name, object type, size, path, or the like. According to this embodiment of the disclosure, the extracted characteristic(s) may be provided as static analysis (SA)-based results 470 to the classification engine 480 for subsequent analysis. Additionally or in the alternative, the static analysis system 430 may analyze the formatted object 426 itself by performing one or more checks. An example of the check may include one or more signature checks, which may involve a comparison of (i) content of the formatted object 426 and (ii) one or more pre-stored signatures associated with detected malware.


It is contemplated that the static analysis system 430 may further include processing circuitry (not shown) that is responsible for extracting or generating metadata contained within or otherwise associated with formatted object 426 from the interface logic 410. This metadata may be subsequently used by the scheduler 435 for initial configuration of one or more VMs 4601-460M within the dynamic analysis system 450, which conducts run-time processing of at least a portion of the formatted object 426 as described below.


Although not shown, for a multiple VM deployment, a first VM 4601 and a second VM 4602 may be configured to run concurrently (i.e. at the same time or in an overlapping manner), where each of these VMs may be initially configured with different software profiles. As an alternative embodiment, the first VM 4601 may be configured to run multiple processes involving a single type of application instance or multiple types of application instances concurrently or sequentially.


More specifically, after analysis of the formatted object 426 has been completed, the static analysis system 430 may provide at least a portion of the formatted object 426 (hereinafter generally referred to as “suspicious object” 428) to the dynamic analysis system 450 for in-depth dynamic analysis by the VMs 4601-460M. For instance, according to one embodiment of the disclosure, a first VM 4601 may be adapted to analyze the suspicious object 428, which may constitute the object itself or a file path for accessing the object for example. Although not shown, it is contemplated that the dynamic analysis may be conducted remotely from the threat detection system 150 that is handling the static analysis, such as within a cloud service 445, or any other remotely located source.


According to one embodiment of the disclosure, the dynamic analysis system 450 features one or more VMs 4601-460M, where each VM 4601, . . . , or 460M processes the suspicious object 428 within a run-time environment. Behavior monitoring logic is configured to be operable with one or more processes running in the VM 4601, . . . , or 460M, where each process may be associated with a different application instance, to collect behavioral information and, in some embodiments, the behavior monitoring logic can be selectively enabled or disabled.


Illustrated in FIG. 4 as an optional feature, the dynamic analysis system 450 may include processing logic 462 that is configured to provide anticipated signaling to the VM 4601-460M during processing of the suspicious object 428, and as such, represents a source of or destination for communications with the suspicious object 428 while processed within that VM 4601, . . . , or 460M. As an example, the processing logic 462 may be adapted to operate by providing simulated key inputs from a keyboard, keypad or touch screen or providing certain other signaling without human involvement, as requested by the suspicious object 428 during run-time.


As shown, the dynamic analysis system 450 further comprises a data store 464 and correlation logic 466. The data store 464 may be used to provide local storage for analysis and detection rules as well as operate as a local log for information accessible to the correlation logic 466 for use in determining whether the object 428 is suspicious. This information may be part of the VM-based results 475 described below.


As shown in FIG. 4, the static analysis system 430 may be adapted to provide SA-based results 470 to the classification engine 480 while the dynamic analysis system 450 may be adapted to provide the VM-based results 475 to the classification engine 480. According to one embodiment of the disclosure, the SA-based results 470 may include information associated with the characteristics of the formatted object 426 that are potentially indicative of malware (e.g., source IP address, object size, etc.). Similarly, the VM-based results 475 may include information associated with monitored behaviors of the suspicious object 428 during processing, which may include abnormal or unexpected system or API calls being invoked, abnormal or unexpected memory accesses by one or more processes running in a first VM 4601.


According to one embodiment of the disclosure, the classification engine 480 is configured to receive the SA-based results 470 and/or the VM-based results 475. Based at least partially on the SA-based results 470 and/or VM-based results 475, the classification engine 480 evaluates the characteristic(s) within the SA-based results 470 and/or the content associated with the monitored behaviors that is part of the VM-based results 475 to determine whether the suspicious object 428 should be classified as “malicious”. This evaluation may be based on data acquired through experiential knowledge or machine learning.


For instance, the classification engine 480 may conduct a probabilistic modeling process that assigns risk levels to different monitored behaviors of the suspicious object 428 being processed within at least a first VM 4601. The risk levels may be aggregated to produce a value (e.g., a probability score or risk designation) that denotes whether the suspicious content 428 is malicious (e.g., associated with an exploit attack). Upon determining that the object 428 is associated with a malicious attack, the classification engine 480 may provide information 490 to identify the malicious object, including information that identifies one or more of the monitored behaviors, to the reporting engine 485.


The reporting engine 485 is configured to receive information 490 from the classification engine 480 and generate alert signals 492, especially in response to the suspicious object 428 being now classified as malicious. The alert signals 492 may include various types of messages, which may include text messages, email messages, video or audio stream, or other types of information over a wired or wireless communication path. The reporting engine 485 features an optional user interface (e.g., touch pad, keyed inputs, etc.) for customization as to the reporting configuration. The reporting engine 485 may further generate signaling 494 directed to the storage (file) system via the monitoring logic to identify that the suspect object is malicious and remediate the storage of a malicious object through removal or quarantining that object.


IV. General Operational Flow


Referring to FIG. 5, an exemplary embodiment of operations conducted during the inter-connectivity between the monitoring logic and the threat detection system of FIGS. 2-4 is shown. Initially, the storage server and the threat detection system are configured (block 500). More specifically, according to one embodiment of the disclosure, the monitoring logic (plug-in) is configured to detect certain communications initiated by the storage control logic of the file system. In particular, the monitoring logic may have access to an API that provides accessibility to notification messages issued by the storage control logic within the storage server. According to this embodiment, the notification messages may operate to acknowledge completion of a requested change in state of a stored object, such as an update of a stored file, storage of a new file on the storage server, or the like.


In response to receipt of an access request message from a remotely located network device for stored content within the storage server, followed by a subsequent storage request message, the file system conducts a state change event (writes the updated file on the storage server, creates and writes a new file on the storage server, etc.) as set forth in block 510. Thereafter, the storage control logic issues a notification message that identifies that the object has been altered, which is detected by the monitoring logic (block 520). The monitoring logic extracts an object identifier from the notification message and establishes communications with the threat detection system, if communications have not yet been established (block 530).


Thereafter, the threat detection system may utilize the communication channel to obtain a copy of the stored object (file) for behavioral analysis to determine whether the stored object includes malware (block 540). In response to a determination that the stored object includes malware, the threat detection system may issue signaling to the file system to remediate the infection by quarantine or removal of the malicious suspect object within the data store of the storage server (block 550). This may involve re-locating the malicious suspect object into a certain portion of memory within the data store and substituting the suspect object with a text object. The text object, when accessed by a user through a network device, causes the display of a message that advises the user of the remediation technique conducted and perhaps information to contact an administrator for the storage server.


In the foregoing description, the invention is described with reference to specific exemplary 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.

Claims
  • 1. A non-transitory computer readable medium including software that, when executed by one or more processor, monitors for a triggering event causing a malware analysis to be conducted on an object and performing operations comprising: an Application Programming Interface (API) provided as an interface to a file system; andmonitoring logic remotely located from the file system, the monitoring logic, when executed by the one or more processors, monitors the API to detect a notification message that is directed to a destination other than the monitoring logic and identifies a state change event representing an activity causing a change in state of a data store associated with the file system to occur, the notification message, at least in part, triggering a malware analysis to be conducted on an object associated with the state change event.
  • 2. The non-transitory computer readable medium of claim 1, wherein the state change event occurs in response to a requested modification of the object stored in the data store of the file system.
  • 3. The non-transitory computer readable medium of claim 1, wherein the state change event occurs in response to a request to store the object into the data store associated with the file system.
  • 4. The non-transitory computer readable medium of claim 1, wherein responsive to detecting the notification message, the triggering of the malware analysis includes extracting an identifier from the notification message, the identifier provides information to identify a storage location of the object in the data store associated with the file system.
  • 5. The non-transitory computer readable medium of claim 4, wherein the identifier includes a file path or a unique name assigned to the object.
  • 6. The non-transitory computer readable medium of claim 1, wherein the triggering of the malware analysis includes recovering the object and conducting a dynamic analysis on the object, the dynamic analysis being conducted by the dynamic analysis system that includes behavior monitoring logic configured to monitor behaviors of one or more virtual machines processing the object recovered from the data store of the file system.
  • 7. The non-transitory computer readable medium of claim 6, wherein the malware analysis further includes conducting a static analysis of the object by a static analysis system, the static analysis includes an analysis of at least one of (i) one or more characteristics associated with the object or (ii) information that is part of the object.
  • 8. The non-transitory computer readable medium of claim 1, wherein the monitoring logic being provided access to storage control logic within the file system via the API to detect the notification message responsive to the state change event being a return message for an access request message, the storage control logic controls storage and retrieval of objects from the file system.
  • 9. The non-transitory computer readable medium of claim 1, wherein the monitoring logic being configured to intercept the notification message where the notification message is directed to a destination other than the monitoring logic.
  • 10. A system comprising: one or more processors; anda memory communicatively coupled to the one or more processors, the memory including a file system including an Application Programming Interface (API) operating as an interface to the file system, andmonitoring logic remotely located from the file system, the monitoring logic, when executed by the one or more processors, monitors the API to detect a notification message that is directed to a destination other than the monitoring logic and identifies a state change event representing an activity causing a change in state of a data store associated with the file system to occur, the notification message, at least in part, triggering a malware analysis to be conducted on an object associated with the state change event.
  • 11. The system of claim 10, wherein the state change event occurs in response to a requested modification of the object stored in the data store of the file system.
  • 12. The system of claim 10, wherein the state change event occurs in response to a request to store the object into the data store associated with the file system.
  • 13. The system of claim 10, wherein responsive to detecting the notification message, the triggering of the malware analysis includes extracting an identifier from the notification message, the identifier provides information to identify a storage location of the object in the data store associated with the file system.
  • 14. The system of claim 13, wherein the identifier includes a file path or a unique name assigned to the object.
  • 15. The system of claim 10, wherein the triggering of the malware analysis includes recovering the object and conducting a dynamic analysis on the object, the dynamic analysis being conducted by the dynamic analysis system that includes behavior monitoring logic configured to monitor behaviors of one or more virtual machines processing the object recovered from the data store of the file system.
  • 16. The system of claim 15, wherein the malware analysis further includes conducting a static analysis of the object by a static analysis system, the static analysis includes an analysis of at least one of (i) one or more characteristics associated with the object or (ii) information that is part of the object.
  • 17. The system of claim 10, wherein the monitoring logic being provided access to storage control logic within the file system via the API to detect the notification message responsive to the state change event being a return message for an access request message, the storage control logic controls storage and retrieval of objects from the file system.
  • 18. The system of claim 10, wherein the monitoring logic being configured to intercept the notification message where the notification message is directed to a destination other than the monitoring logic.
  • 19. A computerized method for monitoring a file system, including an Application Programming Interface (API) operating as an interface, by monitoring logic remotely located from the file system, comprising: monitoring the API to detect a notification message that is directed to a destination other than the monitoring logic; andidentifying a state change event representing an activity causing a change in state of a data store associated with the file system to occur,wherein the notification message, at least in part, triggering a malware analysis to be conducted on an object associated with the state change event.
  • 20. The computerized method of claim 19, wherein the state change event occurs in response to a requested modification of the object stored in the data store of the file system.
  • 21. The computerized method of claim 19, wherein the state change event occurs in response to a request to store the object into the data store associated with the file system.
  • 22. The computerized method of claim 19, wherein responsive to detecting the notification message, the triggering of the malware analysis includes extracting an identifier from the notification message, the identifier provides information to identify a storage location of the object in the data store associated with the file system.
  • 23. The computerized method of claim 22, wherein the identifier includes a file path or a unique name assigned to the object.
  • 24. The computerized method of claim 19, wherein the triggering of the malware analysis includes recovering the object and conducting a dynamic analysis on the object, the dynamic analysis being conducted by the dynamic analysis system that includes behavior monitoring logic configured to monitor behaviors of one or more virtual machines processing the object recovered from the data store of the file system.
  • 25. The computerized method of claim 24, wherein the malware analysis further includes conducting a static analysis of the object by a static analysis system, the static analysis includes an analysis of at least one of (i) one or more characteristics associated with the object or (ii) information that is part of the object.
  • 26. The computerized method of claim 19, wherein the monitoring logic being provided access to storage control logic within the file system via the API to detect the notification message responsive to the state change event being a return message for an access request message, the storage control logic controls storage and retrieval of objects, including the object, from the file system.
  • 27. The computerized method of claim 19, wherein the monitoring logic being configured to intercept the notification message where the notification message is directed to a destination other than the monitoring logic.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No. 14/985,287, filed on Dec. 30, 2015, now U.S. Pat. No. 10,133,866, issued Nov. 20, 2018, the entire contents of this application is incorporated by reference herein.

US Referenced Citations (725)
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 Sørhaug 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
6577920 Hypponen Jun 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
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
8055613 Mu et al. Nov 2011 B1
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
8266295 Klein et al. Sep 2012 B2
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 et al. 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
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
9413782 Adams et al. Aug 2016 B1
9426071 Caldejon et al. Aug 2016 B1
9430646 Mushtaq et al. Aug 2016 B1
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
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 Shiffer 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
9756074 Aziz et al. Sep 2017 B2
9773112 Rathor et al. Sep 2017 B1
9781144 Otvagin et al. Oct 2017 B1
9787700 Amin et al. 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
20020091819 Melchione et al. Jul 2002 A1
20020095607 Lin-Hendel Jul 2002 A1
20020116542 Tarbotton Aug 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
20020194415 Lindsay Dec 2002 A1
20020194490 Halperin et al. Dec 2002 A1
20030021728 Sharpe et al. Jan 2003 A1
20030074574 Hursey Apr 2003 A1
20030074578 Ford et al. Apr 2003 A1
20030079145 Kouznetsov Apr 2003 A1
20030084318 Schertz May 2003 A1
20030101381 Mateev et al. May 2003 A1
20030115483 Liang Jun 2003 A1
20030120952 Tarbotton 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
20040186860 Lee et al. Sep 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
20050114406 Borthakur 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
20080005782 Aziz 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
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
20090038011 Nadathur Feb 2009 A1
20090044024 Oberheide et al. Feb 2009 A1
20090044274 Budko et al. Feb 2009 A1
20090064332 Porras et al. Mar 2009 A1
20090077664 Hsu 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
20100299754 Amit et al. Nov 2010 A1
20100306173 Frank Dec 2010 A1
20110004737 Greenebaum Jan 2011 A1
20110025504 Lyon et al. 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
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
20120296960 Kreuzer Nov 2012 A1
20120297489 Dequevy Nov 2012 A1
20120330801 McDougal et al. Dec 2012 A1
20120331553 Aziz et al. Dec 2012 A1
20130014259 Gribble 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
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 et al. 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
20150220735 Paithane et al. Aug 2015 A1
20150363597 Levine-Fraiman Dec 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
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 et al. 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
Foreign Referenced Citations (11)
Number Date Country
2439806 Jan 2008 GB
2490431 Oct 2012 GB
0206928 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
Non-Patent Literature Citations (85)
Entry
NPL Search Results (Year: 2020).
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.
Liljenstam, Michael , et al., “Simulating Realistic Network Traffic for Worm Warning System Design and Testing”, Institute for Security Technology studies, Dartmouth College (“Liljenstam”), (Oct. 27, 2003).
Lindorfer, Martina, Clemens Kolbitsch, and Paolo Milani Comparetti. “Detecting environment-sensitive malware.” Recent Advances in Intrusion Detection. Springer Berlin Heidelberg, 2011.
Lok Kwong et al: “DroidScope: Seamlessly Reconstructing the OS and Dalvik Semantic Views for Dynamic Android Malware Analysis”, Aug. 10, 2012, XP055158513, Retrieved from the Internet: URL:https://www.usenix.org/system/files/conference/usenixsecurity12/sec12- -final107.pdf [retrieved on Dec. 15, 2014].
Marchette, David J., “Computer Intrusion Detection and Network Monitoring: A Statistical Viewpoint”, (“Marchette”), (2001).
Margolis, P.E. , “Random House Webster's ‘Computer & Internet Dictionary 3rd Edition’”, ISBN 0375703519, (Dec. 1998).
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).
Newsome, J. , et al., “Polygraph: Automatically Generating Signatures for Polymorphic Worms”, In Proceedings of the IEEE Symposium on Security and Privacy, (May 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 Doom, 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).
Spitzner, Lance , “Honeypots: Tracking Hackers”, (“Spizner”), (Sep. 17, 2002).
The Sniffers's Guide to Raw Traffic available at: yuba.stanford.edu/.about.casado/pcap/section1.html, (Jan. 6, 2014).
Thomas H. Ptacek, and Timothy N. Newsham , “Insertion, Evasion, and Denial of Service: Eluding Network Intrusion Detection”, Secure Networks, (“Ptacek”), (Jan. 1998).
U.S. Appl. No. 14/985,287, filed Dec. 30, 2015 Advisory Action dated May 3, 2018.
U.S. Appl. No. 14/985,287, filed Dec. 30, 2015 Final Office Action dated Jan. 30, 2018.
U.S. Appl. No. 14/985,287, filed Dec. 30, 2015 Non-Final Office Action dated Jul. 24, 2017.
U.S. Appl. No. 14/985,287, filed Dec. 30, 2015 Notice of Allowance dated Jul. 12, 2018.
U.S. Pat. No. 8,171,553 filed Apr. 20, 2006, Inter Parties Review Decision dated Jul. 10, 2015.
U.S. Pat. No. 8,291,499 filed Mar. 16, 2012, Inter Parties Review Decision dated Jul. 10, 2015.
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.
“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).
“Packet”, Microsoft Computer Dictionary, Microsoft Press, (Mar. 2002), 1 page.
“When Virtual is Better Than Real”, IEEEXplore Digital Library, available at, http://ieeexplore.ieee.org/xpl/articleDetails.iso?reload=true&arnumber=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).
AltaVista Advanced Search Results. “attack vector identifier”. Http://www.altavista.com/web/results?ltag=ody&pg=aq&aqmode=aqa=Event+Orch- estrator . . . , (Accessed on Sep. 15, 2009).
AltaVista Advanced Search Results. “Event Orchestrator”. Http://www.altavista.com/web/results?ltag=ody&pg=aq&aqmode=aqa=Event+Orch- esrator . . . , (Accessed on Sep. 3, 2009).
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-verlaq Berlin Heidelberg, (2006), pp. 165-184.
Baldi, Mario; Risso, Fulvio; “A Framework for Rapid Development and Portable Execution of Packet-Handling Applications”, 5th IEEE International Symposium Processing and Information Technology, Dec. 21, 2005, pp. 233-238.
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).
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).
Cisco, Configuring the Catalyst Switched Port Analyzer (SPAN) (“Cisco”), (1992).
Clark, John, Sylvian Leblanc,and Scott Knight. “Risks associated with usb hardware trojan devices used by insiders.” Systems Conference (SysCon), 2011 IEEE International. IEEE, 2011.
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).
Crandall, J.R. , et al., “Minos:Control Data Attack Prevention Orthogonal to Memory Model”, 37th International Symposium on Microarchitecture, Portland, Oregon, (Dec. 2004).
Deutsch, P. , “Zlib compressed data format specification version 3.3” RFC 1950, (1996).
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).
Excerpt regarding First Printing Date for Menke Kaeo, Designing Network Security (“Kaeo”), (2005).
Filiol, Eric , et al., “Combinatorial Optimisation of Worm Propagation on an Unknown Network”, International Journal of Computer Science 2.2 (2007).
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.
Gibler, Clint, et al. AndroidLeaks: automatically detecting potential privacy leaks in android applications on a large scale. Springer Berlin Heidelberg, 2012.
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-id/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.
Hjelmvik, Erik , “Passive Network Security Analysis with NetworkMiner”, (IN)Secure, Issue 18, (Oct. 2008), pp. 1-100.
Idika et al., A-Survey-of-Malware-Detection-Techniques, Feb. 2, 2007, Department of Computer Science, Purdue University.
IEEE Xplore Digital Library Sear Results for “detection of unknown computer worms”. Http//ieeexplore.ieee.org/searchresult.jsp?SortField=Score&SortOrder=desc- &ResultC . . . , (Accessed on Aug. 28, 2009).
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.
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).
Krasnyansky, Max , et al., Universal TUN/TAP driver, available at https://www.kernel.org/doc/Documentation/networking/tuntap.txt (2002) (“Krasnyansky”).
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.
Leading Colleges Select FireEye to Stop Malware-Related Data Breaches, FireEye Inc., 2009.
Continuations (1)
Number Date Country
Parent 14985287 Dec 2015 US
Child 16193231 US