Embodiments of the disclosure relate to the field of network security. More specifically, one embodiment of the disclosure relates to a system, apparatus and method for identifying a suspicious object, automatically verifying the suspect object as an exploit through virtual processing.
Over the last decade, malicious software has become a pervasive problem for Internet users as most networked resources include software vulnerabilities that are subject to attack. For instance, over the past few years, more and more vulnerabilities are being discovered in software that is loaded onto network devices, such as vulnerabilities within operating systems for example. While some vulnerabilities continue to be addressed through software patches, prior to the release of such software patches, network resources continue to be the targeted by exploits.
In general, an exploit is information that attempts to take advantage of a vulnerability in computer software by adversely influencing or attacking normal operations of a targeted computer. As an illustrative example, a Portable Execution Format (PDF) file may be infected with an exploit that is activated upon execution (opening) of the PDF file and takes advantage of a vulnerability associated with Acrobat® Reader version 9.0.
Currently, one type of security application widely used for detecting exploits is an intrusion prevention system (IPS). Typically implemented as part of a firewall, an IPS is designed to identify packets suspected of containing known exploits, attempt to block/halt propagation of such exploits, and log/report information associated with such packets through an alert. However, conventional IPS technology suffers from a number of disadvantages.
One disadvantage with conventional IPS technology in that the IPS does not rely on any mechanism to automatically verify its results. Rather, verification of the results produced from a conventional IPS is handled manually.
Another disadvantage is that, without automated verification, the IPS tends to produce a large number of false positives, namely incorrect alerts that occur when the IPS reports certain benign objects as exploits. These false positives cause a variety of adverse effects. For instance, due to the large number of false positives, one adverse effect is that actual exploits detected within network traffic may go unnoticed by an administrator. Other adverse effects may include (i) needless blocking of incoming network traffic; (ii) unnecessarily reduction of processing resources; (iii) significant drainage of administrative resources to handle incorrectly classified objects; and (iv) development of a culture (or policy) of sporadically checking only some of the suspect objects.
In efforts to mitigate the number of false positives, the IPS may frequently require customized and periodic tuning of its signature database, which is a costly endeavor. Furthermore, simply tuning the IPS to significantly reduce the number of false positives can severely degrade the effectiveness of the IPS and/or severely disrupt network operability.
Embodiments of the invention are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings, in which like references indicate similar elements and in which:
Various embodiments of the disclosure relate to an electronic device with network connectivity, such as a threat detection and prevention (TDP) system for example, where the electronic device comprises a static analysis engine, a dynamic analysis engine and reporting logic. According to one embodiment of the disclosure, the static analysis engine comprises intrusion protection system (IPS) logic that conducts at least exploit signature checks and/or vulnerability signature checks on objects under analysis to identify whether characteristics of any of these objects are indicative of an exploit. Those objects with these identified characteristics are label “suspect” or “suspicious” objects. The dynamic analysis engine comprises virtual execution logic to automatically and subsequently analyze, without user assistance, content within suspect objects provided from the IPS logic in order to possibly verify whether any of the suspect objects is an exploit.
Based on analysis results from the IPS logic and the virtual execution logic, reporting logic within the TDP system generates a report (e.g., one or more display screens, printed report, etc.) that highlights information associated with these “verified” exploits, namely suspect objects initially identified by the IPS logic that have been verified by the virtual execution logic to be exploits. Some or all of the information associated with the verified exploits (referred to as “verified exploit information”) may be highlighted to visibly denote the verified exploits from the non-verified exploits, namely suspect objects initially identified by the IPS logic that have not been verified by the virtual execution logic. Examples as to how the verified exploit information is highlighted may include (1) altering location or ordering of at least certain portions of the verified exploit information to prominently display such information within the report; (2) modifying the font (e.g., color, size, type, style, and/or effects) used in conveying some of the verified exploit information; (3) placement of one or more images proximate to a listing of the verified exploit information; or the like.
In the following description, certain terminology is used to describe features of the invention. For example, in certain situations, both terms “logic” and “engine” are representative of hardware, firmware and/or software that is configured to perform one or more functions. As hardware, logic (or engine) may include circuitry having data processing or storage functionality. Examples of such circuitry may include, but is not limited or restricted to a microprocessor, one or more processor cores, a programmable gate array, a microcontroller, an application specific integrated circuit, wireless receiver, transmitter and/or transceiver circuitry, semiconductor memory, or combinatorial logic.
Logic (or engine) may be software in the form of one or more software modules, such as executable code in the form of an executable application, an application programming interface (API), a subroutine, a function, a procedure, an applet, a servlet, a routine, source code, object code, a shared library/dynamic load library, or one or more instructions. These software modules may be stored in any type of a suitable non-transitory storage medium, or transitory storage medium (e.g., electrical, optical, acoustical or other form of propagated signals such as carrier waves, infrared signals, or digital signals). Examples of non-transitory storage medium may include, but are not limited or restricted to a programmable circuit; a semiconductor memory; non-persistent storage such as volatile memory (e.g., any type of random access memory “RAM”); persistent storage such as non-volatile memory (e.g., read-only memory “ROM”, power-backed RAM, flash memory, phase-change memory, etc.), a solid-state drive, hard disk drive, an optical disc drive, or a portable memory device. As firmware, the executable code is stored in persistent storage.
The term “object” generally refers 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 it to be classified for purposes of analysis. During analysis, for example, the object may exhibit a set of expected characteristics and, during processing, a set of expected behaviors. The object may also exhibit a set of unexpected characteristics and a set of unexpected behaviors that may evidence an exploit and potentially allow the object to be classified as an exploit.
Examples of objects may include one or more flows or a self-contained element within a flow itself. A “flow” generally refers to related packets that are received, transmitted, or exchanged within a communication session. For convenience, a packet is broadly referred to as a series of bits or bytes having a prescribed format, which may include packets, frames, or cells.
As an illustrative example, an object may include a set of flows such as (1) a sequence of transmissions in accordance with a particular communication protocol (e.g., User Datagram Protocol (UDP); Transmission Control Protocol (TCP); or Hypertext Transfer Protocol (HTTP); etc.), or (2) inter-process communications (e.g. Remote Procedure Call “RPC” or analogous processes, etc.). Similar, as another illustrative example, the object may be a self-contained element, where different types of such objects may include an executable file, non-executable file (such as a document or a dynamically link library), a Portable Document Format (PDF) file, a JavaScript file, Zip file, a Flash file, a document (for example, a Microsoft Office® document), an electronic mail (email), downloaded web page, an instant messaging element in accordance with Session Initiation Protocol (SIP) or another messaging protocol, or the like.
An “exploit” may be construed broadly as information (e.g., executable code, data, command(s), etc.) that attempts to take advantage of a software vulnerability. Typically, a “vulnerability” is a coding error or artifact of software (e.g., computer program) that allows an attacker to alter legitimate control flow during processing of the software (computer program) by an electronic device, and thus, causes the electronic device to experience undesirable or unexpected behaviors. The undesired or unexpected behaviors may include a communication-based anomaly or an execution-based anomaly, which, for example, could (1) alter the functionality of an electronic device executing application software in a malicious manner; (2) alter the functionality of the electronic device executing that application software without any malicious intent; and/or (3) provide unwanted functionality which may be generally acceptable in another context. To illustrate, a computer program may be considered as a state machine, where all valid states (and transitions between states) are managed and defined by the program, in which case an exploit may be viewed as seeking to alter one or more of the states (or transitions) from those defined by the program.
Malware may be construed broadly as computer code that executes an exploit to take advantage of a vulnerability, for example, to harm or co-opt operation of an electronic device or misappropriate, modify or delete data. Conventionally, malware is often said to be designed with malicious intent. An object may constitute or contain malware.
The term “transmission medium” is a physical or logical communication path between two or more electronic devices (e.g., any devices with data processing and network connectivity such as, for example, a security appliance, a server, a mainframe, a computer such as a desktop or laptop, netbook, tablet, firewall, smart phone, router, switch, bridge, etc.). For instance, the communication path may include wired and/or wireless segments. Examples of wired and/or wireless segments include electrical wiring, optical fiber, cable, bus trace, or a wireless channel using infrared, radio frequency (RF), or any other wired/wireless signaling mechanism.
In certain instances, the terms “detected” and “verified” are used herein to represent that there is a prescribed level of confidence (or probability) on the presence of an exploit within an object under analysis. For instance, the IPS logic (described below) “detects” a potential exploit by examining characteristics or features of an object under analysis, and, in response, determining whether the object has characteristics indicative of an exploit (a “suspect object”). This determination may be conducted through analysis as to whether there exists at least a first probability of the object under analysis being an exploit. Likewise, the virtual execution logic “verifies” the presence of the exploit by monitoring or observing unexpected or anomalous behaviors or activities, and, in response, determining that suspect object is an exploit. According to one embodiment of the disclosure, the determination by the virtual execution logic may involve an analysis as to whether there exists a second probability of the suspect exploit being an exploit. The second probability may be greater than the first probability and may take into account the first probability.
The term “computerized” generally represents that any corresponding operations are conducted by hardware in combination with software and/or firmware. Also, the terms “compare” or “comparison” generally mean determining if a match (e.g., a certain level of correlation) is achieved between two items where one of the items may include a particular signature pattern.
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.
The invention may be utilized for detection, verification and/or prioritization of malicious content such as exploits. As this invention is susceptible to embodiments of many different forms, it is intended that the present disclosure is to be considered as an example of the principles of the invention and not intended to limit the invention to the specific embodiments shown and described.
Referring to
More specifically, a suspected exploit may be detected by conducting exploit signature checks and/or vulnerability signature checks, namely comparing an object under analysis to one or more pre-stored exploit signatures and/or vulnerability signatures to determine if a match is detected. In general, an “exploit signature” includes information directed to a previously detected or known attack pattern while a “vulnerability signature” includes information that characterizes a potential attempt to capitalize on a previously detected or known vulnerability, even when no specific exploit for that vulnerability is known. According to one embodiment of the disclosure, the vulnerability signature may be considered a protocol state machine that maintains state and is normally configured to define parameters for an object being a set of flows that represent an attempt being made to capitalize on a particular software vulnerability that the vulnerability signature is attempting to protect.
Upon conducting at least exploit signature checks and/or vulnerability signature checks on the incoming objects 110 and identifying a first subset of objects 130 having characteristics indicative of an exploit (“suspect objects”), the IPS logic 120 provides the first set of suspect objects 130 to verification logic 150 and provides results 140 of its analysis (referred to herein as “IPS-based results”) to reporting logic 170 for storage and subsequent access.
It is contemplated that the first subset of objects 130 may be lesser in number (and potentially significantly less in number) than the incoming objects 110. For example, while the first subset of objects 130 may be a stream of objects, for ease of discussion in this section, the first subset of objects 130 may refer to at least one incoming object initially suspected of being an exploit (e.g., a suspect object matches a pre-stored exploit signature or a vulnerability signature). Hence, the IPS logic 120 routes the suspect object 130 to verification logic 150 and outputs the IPS-based results 140 associated with suspect object 130 to reporting logic 170.
The IPS-based results 140 may provide details directed to one or more suspected exploits within the suspect object 130. As an example, the details may include (i) an exploit identifier such as a particular name/family of the suspected exploit (if known); (ii) source address (e.g., Uniform Resource Locator “URL”, Internet Protocol “IP” address, etc.) of the electronic device sending the suspect object; (iii) time of analysis; (iv) information associated with anticipated anomalous activities that may be conducted by the suspected exploit; (v) information regarding anticipated communication deviations from the protocol applicable to the network traffic; and/or (vi) recommended remediation techniques for this type of exploit.
As mentioned above, the suspect object 130 is routed to verification logic 150 (e.g., virtual execution logic being part of a dynamic analysis engine 270 as illustrated in
The results 160 of this analysis are output from the verification logic 150 for subsequent use by reporting logic 170 in generating a report 180 that visibly denotes and filters the suspect objects from the first set of objects 130 that have been verified (verified exploits) from those suspect objects from the first set of objects 130 that have not been verified (non-verified exploits). Although not illustrated in
Thereafter, at least portions of the IPS-based results 140 and the VM-based results 160 for the suspect object are combined. More specifically, in the event that the VM-based results 160 indicate that the verification logic 150 failed to verify that the suspect object 130 is an exploit (e.g., a computed score below a prescribed threshold), some or all of the IPS-based results 140 and the VM-based results 160 for that object are combined and added as part of “non-verified exploit information” 190 for storage and use by the reporting logic 170.
However, when the VM-based results 160 indicate that the verification logic 150 has verified that the suspect object 130 is an exploit (e.g., the computed score is equal to or above a prescribed threshold), some or all of the IPS-based results 140 and the VM-based results 160 may be modified to achieve a highlighted display of at least the verified exploits. For example, certain portions of the results 140 and/or 160 may be associated with display commands, which are recognized by a display controller being part of display logic within the reporting logic 170 and causes the display logic to produce an output that may visibly denotes differences between displayed results associated with verified exploits from displayed results associated with the non-verified exploits. This exploit information associated with the verified exploit may be stored as part of the “verified exploit information” 195″.
The display logic 290 also may be configured to recognize that the verified exploit information 195 is to be displayed more prominently than the non-verified exploit information 190. For instance, display logic 290 may be configured to prominently display the verified exploit information within different display screens, within different display windows, within a certain section of a display screen, or positioned at a top of a listing. Additionally or in the alternative, at least a portion of the verified exploit information for each verified exploit may be conveyed using a different font (e.g., color, size, type, style, and/or effects) than the font used for conveying exploit information associated with non-verified exploits. Additionally or in the alternative, one or more images may be placed proximate to exploit information associated with each verified exploit. Illustrative examples of screen displays are shown in
Besides displaying the exploit information, the reporting logic 170 may issue an alert (e.g., by email or text message) to security administrators for example, communicating the urgency in handling one or more verified exploits. The reporting logic 170 may also issue alerts for one or more non-verified exploits by providing alerts in a manner that denotes to users a selected threat level.
As further shown, the IPS logic 120 may be communicatively coupled to a network 105 (e.g., public or private network) to receive incoming objects 110, such as one or more flows for example, destined for a particular client device. The IPS logic 120 is configured to conduct exploit signature checks and/or vulnerability signature checks on the incoming objects 110 to determine whether any of the objects 110 have characteristics indicative of an exploit, and thereafter, provide the suspect object(s) 130 to verification logic 150.
According to one embodiment of the disclosure, the communicative coupling between the IPS logic 120 and the verification logic 150 is provided in a sideband configuration, where the suspect object(s) 130 (or a copy thereof) may be temporarily stored and processed in the verification logic 150 concurrently with analysis of other objects by the IPS logic 120. This allows for the detection of exploits through a longer duration of analysis by the verification logic 150 (e.g., longer processing and monitoring of processing of the suspect object 130 within the virtual execution logic). This also allows detection of exploits with delayed activation, including time-bombs. However, it is contemplated that the IPS logic 120 may be configured in-line with verification logic 150 as shown in
Referring to
Herein, according to the embodiment illustrated in
As shown, the first TDP system 2101 may be communicatively coupled with the communication network 230 via a network interface 238. In general, the network interface 238 operates as a data capturing device (sometimes referred to as a “tap” or “network tap”) that is configured to receive data propagating to/from the client device 234 and provide at least some of this data to the first TDP system 2101. Alternatively, as shown in
According to one embodiment of the disclosure, the network interface 238 is capable of receiving and routing objects associated with network traffic to the first TDP system 2101. The network interface 238 may provide the entire object or certain content within the object, for example, one or more files that are part of a set of flows, packet payloads, or the like. In some embodiments, although not shown, network interface 238 may be contained within the first TDP system 2101.
According to an embodiment of the disclosure, the network interface 238 may be further configured to capture metadata from network traffic intended for client device 234. According to one embodiment, the metadata may be used, at least in part, to determine protocols, application types and other information that may be used by logic within the first TDP system 2101 to determine particular software profile(s). The software profile(s) are used for selecting and/or configuring a run-time environment in one or more virtual machines selected or configured as part of the dynamic analysis engine 270, as described below. However, according to another embodiment, a “matched” vulnerability signature may be used for VM configuration to specify software profile(s) (or corresponding software image(s)) having the specific vulnerability associated with the matched vulnerability signature. These software profile(s) may be directed to different versions of the same software application for fetching corresponding software image(s) from storage device 265.
It is contemplated that, for any embodiments where the first TDP system 2101 is implemented as an dedicated appliance or a dedicated computer system, the network interface 238 may include an assembly integrated into the appliance or computer system that includes a network interface card and related logic (not shown) for connecting to the communication network 230 to non-disruptively “tap” network traffic propagating through firewall 236 and provide either a duplicate copy of at least a portion of the network traffic or at least a portion the network traffic itself to a static analysis engine 250. In other embodiments, the network interface 238 can be integrated into an intermediary device in the communication path (e.g., firewall 236, router, switch or other networked electronic device, which in some embodiments may be equipped with SPAN ports) or can be a standalone component, such as an appropriate commercially available network tap. In virtual environments, a virtual tap (vTAP) can be used to duplicate files from virtual networks.
As further shown in
In some embodiments, as shown in
In general, referring to
Upon detecting a match during the exploit signature check and/or the vulnerability signature check (an object under analysis has characteristics that suggest the object is an exploit), the IPS logic may be adapted to upload the IPS-based results 140 for storage in database 255. These results 140 may include, but are not limited or restricted to (i) an exploit identifier such as a particular name/family of the suspected exploit (if known); (ii) source address (e.g., Uniform Resource Locator “URL”, Internet Protocol “IP” address, etc.) of a source of the suspect object; (iii) time of analysis; (iv) information associated with anticipated anomalous activities that may be conducted by the suspect exploit; (v) information regarding anticipated communication deviations from the protocol applicable to the network traffic; and/or (vi) recommended remediation techniques. The IPS-based results 140 may be accessible by classification logic 285 and/or display logic 290, as described below.
Furthermore, the IPS logic 120 routes suspect object to the virtual execution logic 150 within dynamic analysis engine 270. The dynamic analysis engine 270 is configured to provide more in-depth analysis of suspect object(s) from the IPS logic 120 by analyzing the content of the suspect object(s) in order to verify whether or not the suspect object is an exploit. Additionally, according to one embodiment of the disclosure, a tag value may accompany or be associated with the suspect object for use in subsequently locating the suspect object's corresponding stored IPS-based results 140 after virtual processing within the dynamic analysis engine 270. For instance, the tag value may be an address, an index number, or the like. It is contemplated that tag value may be separate from the suspect object or may be strategically placed within the suspect object itself (e.g., within a header portion, payload, etc.).
More specifically, after static scanning has been completed, the IPS logic 120 provides the suspect object to the dynamic analysis engine 270 for in-depth dynamic analysis using virtual machines (VMs) 2751-275M (M≧1). For instance, the dynamic analysis engine 270 may simulate transmission and/or receipt by a destination device comprising the virtual machine. Of course, if the object is not suspected of being an exploit, the IPS logic 120 may simply store the IPS-based results within database 255 and denote that the object is benign.
According to one embodiment, one or more VMs 2751-275M within the virtual execution environment 272 may be configured based on the results of the exploit signature check and the vulnerability signature check conducted by the IPS logic 120. For instance, for an unknown vulnerability, the VMs 2751-275M may be configured with all of the software profiles corresponding to the software images stored within storage device 265. Alternatively, the VMs 2751-275M may be configured according to a prevalent software configuration, software configuration used by an electronic device within a particular enterprise network (e.g., client device 234), or an environment that is required for the object to be processed, including software such as a web browser application, PDF™ reader application, or the like. However, for a known vulnerability which occurs after a successful match during a vulnerability signature check, the VMs 2751-275M may be more narrowly configured to software profiles associated with vulnerable software.
As a first illustrative example, upon determining that the suspect object matches a particular vulnerability signature, the scheduler 260 may determine (1) what vulnerability signature has been tagged; (2) if the vulnerability is a server side vulnerability or client side vulnerability; and/or (3) which software image(s) are associated with software having the vulnerability associated with the tagged vulnerability signature. Thereafter, the software profile(s) are selected by the scheduler 260 to fetch these software image(s) for configuration of VM 2751. This tailored selection scheme avoids VM configuration for software that does not feature the matched (tagged) software vulnerability.
As a second illustrative example, the scheduler 260 may be adapted to configure the multiple VMs 2751-275M for concurrent virtual execution of a variety of different versions of the software in efforts to verify that the suspect object identified by the signature matching logic 253 is an exploit.
Of course, it is contemplated that the VM configuration described above may be handled by logic other than the scheduler 260. For instance, although not shown, the static analysis engine 250 may include configuration logic that is adapted to determine (1) what vulnerability signature was tagged; (2) if the vulnerability is a server side vulnerability or client side vulnerability; and/or (3) which software image(s) are associated with software having the vulnerability associated with the tagged vulnerability signature. This configuration logic may transmit the VM configuration information to the scheduler 260 and/or dynamic analysis engine 270 to handle VM configuration as described above.
According to one embodiment of the disclosure, the dynamic analysis engine 270 is adapted to execute one or more VMs 2751-275M to simulate the receipt and execution of content associated with an object under analysis within a run-time environment as expected by the type of object. For instance, dynamic analysis engine 270 may optionally include a protocol sequence replayer (replay logic) 280 to replay the suspect object and provide replayed data flows to the VM(s) 2751, . . . , and/or 275M or object extractor logic 282 to extract a self-contained object within a data flow for virtual processing by VM(s) 2751, . . . , and/or 275M. One embodiment of the protocol sequence replayer is described in U.S. Pat. No. 8,375,444, the entire contents of which are incorporated by reference herein.
For example, the replay logic 280 may be adapted to provide, and sometimes modify (e.g. modify IP address, etc.) packets associated with the suspect objects and synchronize any return network traffic generated by the virtual execution environment 272 in response to the packets. Hence, the replay logic 280 may suppress (e.g., discard) the return network traffic such that the return network traffic is not transmitted to the communication network 230. According to one embodiment of the disclosure, for a particular suspect object being a flow such as a TCP or UDP sequence, the replay logic 280 may replay the data packets by sending packets to the virtual execution environment 272 via a TCP connection or UDP session. Furthermore, the protocol sequence replay logic 280 synchronizes return network traffic by terminating the TCP connection or UDP session.
As further shown in
According to one embodiment of the disclosure, the score determination logic 278 comprises one or more software modules that are used to determine a probability (or level of confidence) that the suspect object is an exploit. Score determination logic 278 is configured to generate a value (referred to as a “score”) that classifies the threat of the possible exploit. Of course, a score may be assigned to the suspect object as a whole by mathematically combining the scores determined by analysis of different content associated with the same suspect object to obtain an overall score for that suspect object. Thereafter, the suspect object and/or score are routed to classification logic 285 for use in prioritization.
In general, the classification logic 285 may be configured to receive the VM-based results 160. According to one embodiment of the disclosure, the score may be used, at least in part, to determine whether the virtual execution logic 150 has verified that the suspect object is an exploit. Where the score represents that the suspect object 130 has not been verified by the virtual execution logic 150 to have the characteristics of an exploit, some or all of the VM-based results 160 may be combined with its corresponding IPS-based results to produce the non-verified exploit information 190, which is stored in database 255.
However, if the score represents that the suspect object 130 has been verified by the virtual execution logic 150 as an exploit, at least some of the combined IPS-based results 140 and/or the VM-based results 160 may be modified by the classification logic 285 and subsequently stored as at least part of the verified exploit information 195. Stated differently, the classification logic 285 operating with the database 255 may be responsible for prioritizing the display of exploit information associated with the verified exploits. This may involve the classification logic 285 modifying order or position for the displayed verified exploit information, or adding information to the verified exploit information that is used by the display logic 290 to modify display order or positioning; modifying the type of font (e.g., color, size, type, style, and/or effects) used for text conveying certain verified exploit information; placing one or more images proximate to verified exploit information for each verified exploit; or the like.
Of course, it is contemplated that other parameters, combined with or separate from the score, may be used or relied upon to determine whether and/or how to highlight display of the exploit information associated with the suspect object.
Thereafter, along with non-verified exploit information 190, the verified exploit information 195 is stored within database 255 and accessible by display logic 290.
More specifically, according to one embodiment of the disclosure, classification logic 285 comprises prioritization logic 286 and tag image generation logic 288. According to one embodiment of the disclosure, the prioritization logic 286 may be adapted to modify (e.g., alter or associate display commands to) exploit information associated with verified exploits based one or more factors, including (i) score associated with the object; (ii) source of the object; (iii) repeated detection of the same exploit in different suspect objects; or the like. This modification may involve modifying font (e.g., color, size, type, style, and/or effects) used to convey the exploit information associated with verified exploits. As another example, this modification may involve classification and storage of the exploit information as verified exploit information 195 which, when accessed by the display logic 290, places the exploit information associated with the verified exploit at a specific location on a display screen or within display image (e.g., within a specific window or display screen listing the verified exploits, at a particular order within the listing of the verified and non-verified exploits, etc.).
Of course, as an alternative, the display logic 290 may be implemented with some or all of the functionality associated with the prioritization logic 286 and/or tag image generation logic 288 in lieu of deployment within the classification logic 285. Hence, responsive to information received from the classification logic, the display logic 290 may be adapted to modify exploit information associated with verified exploits.
The tag image generation logic 288 may be adapted to operate in combination with the prioritization logic 286 to generate a tag image (not shown), which is included as part of the verified exploit information 195 associated with suspect object for display. The tag image is used to provide another visual representation of the presence of a verified exploit, namely a suspected exploit detected by the IPS logic 120 whose presence has been verified by the virtual execution logic 150.
Of course, in lieu of or in addition to static scanning operations being conducted by TDP systems 2101-2103, it is contemplated that cloud computing services 240 may be implemented with IPS logic 120 to perform the exploit and/or vulnerability signature checks and/or with virtual execution logic 150 to conduct virtual execution on content within the object under analysis, as described herein. The display logic 290 may cause the display of the exploit information associated with the verified exploits and/or non-verified exploits graphically or otherwise through a downloaded page or pages from the cloud computing services 240 to a client device or to an application running on a client device that displays the results obtained from the cloud computing services 240. In accordance with this embodiment, TDP system 2101 may be adapted to establish secured communications with cloud computing services 240 for exchanging information.
Referring now to
Referring now to
Processor(s) 300 is further coupled to persistent storage 330 via transmission medium 325. According to one embodiment of the disclosure, persistent storage 330 may include (i) static analysis engine 250, including first analysis logic (e.g., IPS logic) 250; (ii) the dynamic analysis engine 270, including virtual execution logic 272, monitoring logic 276, score determination logic 278 along with optional replay and object extractor logic 280 and 282; (iii) classification logic 285 including prioritization logic 286 and tag image generation logic 288; and (iv) display logic 290. Of course, when implemented as hardware, one or more of these logic units could be implemented separately from each other.
IPS logic 120 comprises one or more software modules that conduct a first static analysis on each incoming object. As described above, this analysis may involve performing at least exploit signature checks and vulnerability signature checks on each incoming object to determine whether characteristics of any of these objects are indicative of an exploit. If not, the analysis may be discontinued for the object, or the object may be provided for non-real time forensic review. Upon confirming that one or more suspect objects have characteristics of an exploit, the IPS logic 120 provides the suspect object(s) to the virtual execution logic 150. It is contemplated that a tag value, if used, may accompany (or be associated with) the suspect object to identify a stored location of the IPS-based results 140 for the suspect object, as described above. The IPS-based results 140 are uploaded to data store 350, at least partially operating as a database, for subsequent access by classification logic 285.
Virtual execution environment 272 comprises one or more software modules that are used for performing an in-depth, dynamic and real-time analysis of the suspect object using one or more VMs. More specifically, the virtual execution environment 272, protocol sequence replay logic 280 and/or object extractor logic 282 are adapted to run the VM(s), which virtually processes the content associated with the suspect objects by simulating receipt and execution of such content in order to determine the presence of one or more exploits. Furthermore, the monitoring logic 276 monitors in real-time and may also log at least anomalous behaviors by the VM(s) configured with certain software and features that are presumably targeted by the matched exploit or vulnerability. In essence, the monitoring logic 276 identifies the effects that the suspect object would have had on a physical electronic device with the same software/feature configuration. Such effects may include unusual network transmissions, unusual changes in performance, and the like.
Thereafter, according to the observed behavior of the virtually executed content, the monitoring logic 276 may determine that the content is associated with one or more exploits, where the severity of the observed anomalous behavior and/or the likelihood of the anomalous behavior results from an exploit, is evaluated and reflected in a “score” assigned by the score determination logic 278. As a result, these logic units collectively output the VM-based results 160 for use by classification logic 285 to highlight exploit information associated with verified exploits.
The prioritization logic 286 comprises one or more software modules that are used to highlight information associated with verified exploits, namely the verified exploit information 195. For instance, the prioritization logic 286 may assign higher priority to exploit information directed to verified exploits, where the priority may be used by the display logic 290 to determine an order or location for display. Furthermore, the prioritization logic 286 may be adapted to modify the font used in display of the verified exploit information (e.g., color, size, type, style, and/or effects), or control the placement of one or more images provided by the tag image generation logic 288 proximate to its corresponding exploit information.
Continuing the above example, processor(s) 300 may invoke display logic 290, which produces one or more screen displays for conveying a detailed summary of verified and/or non-verified exploits detected by the TDP system 2101. According to one embodiment of the disclosure, the information associated with the verified exploits (verified exploit information 195) may be presented in a first area of a display screen while information associated with the non-verified exploits (non-verified exploit information 190) may be presented in a second area of the display screen. As another example, the verified exploit information 195 may be presented as top entries in a listing of all exploits detected by the IPS logic while the non-verified exploit information 190 is presented subsequently. As another example, some or all of the verified exploit information 195 may be presented in different font (e.g., different type, color, style such as bold or italic, effects such as underlining or shadow, etc.) than font used for conveying the non-verified exploit information 190. As yet another example, a tag image may be positioned next to the verified exploit information 195 unlike non-verified exploit information 190 associated with suspect objects.
Referring to
Although not shown, when determining that the suspect object has characteristics of a suspected exploit, the IPS logic may be configured to block the object from proceeding to the targeted client device, although blocking may be delayed until completion of the VM-based analysis to mitigates errors due to false positives. This blocking functionality may be adjusted by the network administrator based on the severity/type of suspected exploit, number of occurrences of this type of exploit within a prescribed time period, or the like. Furthermore, prior to performing further exploit analysis, if used, a tag value may accompany (or being associated with) the suspect object when output from the IPS logic so that the IPS-based results for the suspect object can be related to the subsequent VM-based results for that object.
After IPS-based analysis for the suspect object has concluded, the content of the suspect object may undergo VM-based analysis (blocks 415 and 420). The results of the VM-based analysis (VM-based results) are provided for subsequent review (block 425). According to one embodiment of the disclosure, the classification logic performs such review, although in the alternative, logic within the dynamic analysis engine may conduct this review.
Normally, if the VM-based analysis fails to verify that the suspect object is an exploit, a score may be assigned to denote that no exploit has been detected (block 430). In this case, information produced during the VM analysis of the suspect object along with its corresponding IPS-based results are stored as part of the non-verified exploit information (block 435). However, during virtual execution of the object, if the monitored behavior denotes that the suspect object is an exploit, a score is assigned that represents the likelihood and/or threat level for the “verified” exploit(s).
According to one embodiment of the disclosure, the classification logic may be configured to obtain the IPS-based results associated with the verified exploit, where some or all of the information from the IPS-based results and the VM-based results may be prominently displayed (highlighted) as illustrated in blocks 440 and 445. Such highlighting may include (i) assigning a specific display location for exploit information associated with verified exploits that is different from exploit information associated with non-verified exploits; (ii) modifying the presentation (e.g., font type, color, style, etc.) of exploit information associated with verified exploits where the exploit information associated with the non-verified exploits will have a different presentation; (iii) controlling placement of one or more images proximate to exploit information associated with verified suspect objects only. Other display adjustments may be used, as this highlighting is conducted to visibly differentiate exploit information associated with the verified exploits from exploit information associated with the non-verified exploits.
Thereafter, the (highlighted) verified exploit information is uploaded into the database for storage and now accessible by display logic for rendering (blocks 450 and 455).
Referring now to
More specifically, according to one embodiment of the disclosure, a first area 510 displays a plurality of entries 5201-520R (R≧1, R=6 for this embodiment) that provide information directed verified exploits and/or non-verified exploits. As shown, each row of entries (e.g., 5201) rendered by the display logic comprises a plurality of fields, including one or more of the following: (1) a name 521 of the exploit associated with a suspect object; (2) a signature pattern 522 applicable to the object under analysis; (3) addressing information 523 (e.g., Internet Protocol “IP” address, Media Access Control “MAC” address, etc.) for a source device providing the verified or non-verified exploit; (4) a level of severity 524 (e.g., high, medium, low) of the detected exploit, where the severity level corresponds, at least in part, to the threat score; (5) a time 525 during which the exploit analysis process was conducted; and/or (6) name and/or version number 526 of software detected to be vulnerable to the detected exploit.
A second area 530 may be configured with one or more images corresponding to each entry for a verified exploit, namely an object initially identified by the IPS logic as having characteristics indicative of an exploit and verified of being an exploit by the virtual execution logic. For instance, as illustrated in
It is noted that the mere existence of a verified exploit may warrant heightened severity level, but does not require heightened severity levels as illustrated by the fact that certain non-verified exploits may be assigned higher severity levels than some verified exploits. Rather, exploit information associated with the verified exploits is highlighted, namely this exploit information is displayed more prominently than exploit information associated with non-verified exploits for example. This allows a network administrator to more quickly and easily determine verified exploits and thereby substantially mitigate administrative and operational disadvantages from false-positives.
As an example, as a highlighting technique, the font associated with the exploit names (HTTP Exploit_ID1; HTTP Exploit_ID2; Java Exploit_ID1; and HTML Exploit_ID1) may be displayed differently than the font associated with the exploit names for non-verified exploits (e.g., Java Exploit_ID2). Alternatively, the verified exploit information associated with the verified exploits may be ordered at the top of the listing (see
Furthermore, although not shown, it is contemplated that selection of a portion of the entry (e.g., entries within fields 521/522/523/524/526 (as represented by an underlined portion) and/or a separate “Details” field 540) may enable the network administrator to obtain more detailed information of the exploit and/or analysis associated with that exploit.
For instance, by selecting the particular listed exploit 521, the administrator may be able to uncover family and other information related to the exploit (e.g., documented attacks, recommended remediation techniques, targeted client device(s), etc.). Also, by selecting the signature 522, the administrator may have access to additional information concerning what signature (exploit, vulnerability, etc.) was determined by the IPS to match the suspect object. Additional information (e.g., information on signature updates, detection history of this signature with other objects, etc.) may be provided as well.
Similarly, by selecting the corresponding host address 523 or the severity level 524, the administrator may be provided with additional information directed to geographic location of the source of the suspect object corresponding to that exploit, addressing information directed to intermediary devices that received the suspect object, the particular network operations targeted by the exploit, or the like. Also, by selecting the software type 526, a listing of all software types detected to be vulnerable to the verified exploit (along with video/images of monitored anomalous behaviors denoting the presence of such exploit) may be accessed.
Referring now to
As shown, similar to the first user interface display screen 500, first area 550 of the second user interface display screen 545 displays a plurality of entries 5601-560S (S≧1, S=4 for this embodiment) that provides information directed to verified exploits. Each of the entries (e.g., 5601) rendered by the display logic comprises: (1) a name 561 of the verified exploit (suspect object verified to be an exploit); (2) a signature 562 that initially identified the suspect object as having characteristics indicative of an exploit; (3) addressing information 563 (e.g., Internet Protocol “IP” address, Media Access Control “MAC” address, etc.) for a source device providing the detected exploit; (4) a level of severity 564 (e.g., high, medium, low) of the detected exploit that corresponds, at least in part, to the threat score; (5) a time 565 during which the exploit analysis process was conducted; and/or (6) name and/or version number 566 of software detected to be vulnerable to the detected exploit.
As shown, a second area 570 may be provided, which comprises an image corresponding to each entry that is associated with the verified exploits, as described above. As illustrated in
A third area 580 illustrates exploit information associated with non-verified exploits named “Java Exploit_ID2”, “RPC Exploit_ID1” for example.
According to an alternative embodiment of the disclosure, the static analysis engine may be configured with a first static analysis logic (e.g., IPS logic) and a second static analysis logic (e.g., heuristic logic), which is configured to operate independently from the IPS logic and identifies whether characteristics of any of the incoming objects are indicative of an exploit. As described below, the first static analysis logic and the second static analysis logic may operate in parallel or in tandem.
In particular, as described above, the first static analysis logic (IPS logic) conducts at least exploit signature checks and/or vulnerability signature checks on the incoming objects to identify a first subset of objects having characteristics indicative of an exploit. The second static analysis logic (heuristic logic) may be configured to analyze the same or different objects, where such analysis may be in accordance with at least a set of rules and/or signatures different than those utilized by the first static analysis logic (IPS logic).
More specifically, according to this embodiment of the invention, upon identifying the suspect objects (first subset of objects), the first static analysis logic (IPS logic) provides suspect objects, perhaps each accompanied by or associated with a tag identifier (hereinafter referred to as “tag_ID1”), to the verification logic 150 of
The second static analysis logic (heuristic logic) is configured to analyze the incoming objects to determine whether the presence, absence or modification of information within an object may denote potential malicious activity indicating that object may be an exploit. Such determination may involve the second static analysis logic (heuristic logic) conducting operations to determine whether certain portions of the object corresponds to one or more “malicious identifiers,” which may include, but are not limited or restricted to a particular source or destination address (e.g., URLs, IP addresses, MAC addresses, etc.) that is associated with known exploits; exploit patterns; or shell code patterns.
Additionally, with each suspect object, the heuristic logic may provide a tag identifier (tag_ID2) for use in locating corresponding heuristic-based results 640 associated with each suspect object 630. Hence, tag_ID2 may be further used to identify to other logic that this suspect object originated from the heuristic logic 620.
After either the first static analysis logic (IPS logic) or the second static analysis logic determine which of the incoming objects have characteristics indicative of an exploit, the suspect objects are provided to the virtual execution logic for more in-depth dynamic analysis using one or more virtual machines (VMs). Such dynamic analysis may include virtual execution of the content of the suspect objects with one or more configured VMs, as described above. The behaviors of the VM(s) are monitored for detection of anomalous or unexpected activity.
It is contemplated that the first static analysis logic (IPS logic) and the second static analysis logic (heuristic logic) may operate in parallel in which both of these logic units conduct the preliminary exploit detection analysis on the same suspect objects. More specifically, the second static analysis logic (heuristic logic) may conduct its analysis on an object extracted from the network traffic concurrently (i.e. at least partially overlapping in time) with the analysis of the same object by the IPS logic. This provides the TDP system with an ability to account for false negatives that signify a lack of detection of an exploit by the IPS logic. Also, such parallel analysis may be conducted in order to increase scrutiny of network traffic for objects originating from a certain geographic location prone to exploits, from a certain IP addresses that have been identified as a malicious source, or the like.
Of course, it is contemplated that the first static analysis logic (IPS logic) and second static analysis logic (heuristic logic) may operate in tandem in which an incoming object is capable of being processed by either the IPS logic or the heuristic logic within the embodiment. Control of the selection as to whether the static analysis is performed by the first static analysis logic (IPS logic) or the second static analysis logic (heuristic logic) may be assigned to additional control logic within the static analysis engine. Such control may be based on the type of object under analysis, source, traffic conditions, or the like.
Referring to
Upon identifying that a first subset 610 of the incoming objects 110 are “suspicious” (e.g., one or more objects 110 match an exploit signature and/or vulnerability signature), the IPS logic 120 subsequently routes the first subset of suspect objects 610 to the verification logic 150 (e.g., virtual execution logic). Each of these objects may be accompanied by a tag identifier (tag_ID1) and provided to the verification logic 150.
Besides being used for subsequently locating the IPS-based results 140 associated with the suspect object (provided from the IPS logic 120 to the reporting logic 170), tag_ID1 may be used to additionally to identify to the verification logic 150 and/or reporting logic 170 that these suspect objects 610 are provided from the IPS logic 120. Such information may be useful for identifying exploit information associated with verified exploits originating from the IPS logic, where this exploit information may be highlighted even differently than exploit information associated with verified exploits originating from a second static analysis logic 620.
Operating in tandem or in parallel with IPS logic 120, the second static analysis logic 620 (e.g., heuristic logic) conducts another type of static analysis on some or all of the objects 110 to produce a subset of objects 630 having characteristics indicative of an exploit. Hence, when operating in parallel, heuristic logic 620 may receive the incoming objects 110, which are also being received and analyzed by IPS logic 120. When operating in tandem with the IPS logic 120, the heuristic logic 620 may receive some or all of the incoming objects 110, where the switching between receipt of specific incoming objects by either the IPS logic 120 or the heuristic logic 620 may be conducted by switching logic 645 via control signals 647 from scheduler 260 or some other logic within TDP system 2101, as shown in
The suspect objects 610 and/or 630 (collectively referred to as “suspect objects 635”), detected by the IPS logic 120 and/or heuristic logic 620, are routed to the verification logic 150. The verification logic 150 is adapted to verify whether any of the suspect objects is an exploit through virtual processing of the content within these objects 635. The VM-based results 650 of this analysis are output from the verification logic 150 for subsequent use by reporting logic 170 for display purposes, as described above.
More specifically, the first static analysis logic (e.g., IPS logic 120) conducts at least exploit signature checks and/or vulnerability signature checks to identify whether characteristics of any of the analyzed objects 110 are indicative of an exploit. If so, the IPS logic 120 forwards these suspect object(s) 610 to the verification logic 150.
Additionally, one or more heuristic checks may be conducted on some or all of objects 110, including various scanning operations conducted on portions of the objects to determine correspondence with one or more malicious identifiers, as described above. While the IPS logic 120 is adapted to identify objects in accordance with at least exploit signature checks and/or vulnerability signature checks, the heuristic checks are directed to a more expansive static analysis of some or all of objects 110, including the use of different types of signatures or other static analysis schemes.
After performing the heuristic check(s) by the heuristic logic 620, a second set of suspect objects 630 is provided to the verification logic 150. Again, the second set of objects 630 may be lesser (and potentially significantly less) in number than the incoming objects 110.
After virtual processing of content within each of the suspect objects 610 and/or 630, and thereafter verifying that particular objects are exploits (verified exploits), the verification logic 150 provides VM-based results 650 that may be modified, along with its corresponding IPS-based results 140, to generate a report 660 (e.g., one or more display screens, printed report, etc.). The report 660 is configured to visibly highlight exploit information associated with verified exploits. As an alternative, the report 660 may also be configured to visibly highlight exploit information associated with verified exploits from exploit information associated with non-verified exploits (suspect objects having characteristics of exploits that were not verified by the VMs).
Referring to
Operating in parallel or tandem with IPS logic 120, the heuristic logic 620 is configured to conduct one or more heuristic checks on objects under analysis. These heuristic checks may be considered more expansive in analysis than the exploit and/or vulnerability checks conducted by the IPS logic 120 as mentioned above.
Herein, based on the results of the heuristic checks conducted by heuristic logic 620, score determination logic 720 determines the probability (or level of confidence) that the characteristics of the analyzed object are indicative of an exploit. In other words, score determination logic 720 is configured to generate a value that classifies the threat level of the possible exploit characterized by each of the analyzed objects. For instance, if the heuristic checks detect one type of characteristic that suggests the object under analysis is an exploit, the object may be classified with a first threat level. The first threat level may be represented by a score (value) corresponding to the likelihood of the object being an exploit (e.g., score of 3 out of 10). However, if the heuristic checks detect multiple characteristics or another type of characteristic that more strongly suggests the object under analysis is an exploit, a higher score (e.g., score of 8 out of 10) may be assigned by score determination logic 720 to denote a higher probability of the detected presence of an exploit.
Thereafter, the objects and their corresponding scores may be routed from the static analysis engine 750 to the dynamic analysis engine 270 for use in further analysis to verify which of the suspect objects, if any, are exploits. Additionally or in the alternative, it is contemplated that the score may be provided to classification logic 785 for use in prioritization.
More specifically, after static scanning has completed, the object may be provided to the dynamic analysis engine 270 for in-depth dynamic analysis using virtual machines (VMs) 2751-275M (M≧1). Of course, if the characteristics of the object are not indicative of an exploit, the heuristic logic 620 may halt further analysis of content with the object.
In general, besides receiving VM-based results 160 from dynamic analysis engine 270, the classification logic 785 may be configured to receive assigned scores from static analysis engine 750. Classification logic 785 may be configured to mathematically combine the scores assigned to content associated with the suspect object (based on findings from static analysis engine 750 and dynamic analysis 270) to obtain an overall score that is assigned with the verified or non-verified exploit.
According to one embodiment of the disclosure, the overall score may be used, at least in part, to identify verified exploits from non-verified exploits. Also, the score may be used, at least in part, for highlighting operations such as assigning a display priority that may influence the display ordering as described above. However, it is contemplated that other parameters, combined with or separate from the score assigned to the exploit, may be used to classify exploits or influence display priority. For instance, the overall score along with other parameters, such as the presence of the tag_ID1 or tag_ID2 as part of exploit information included in the VM-based results, may influence the display ordering of that exploit.
Referring now to
Referring to
The IPS logic and heuristic logic may be configured to operate in parallel (or in tandem) based on factors that may warrant increased scrutiny in efforts to detect exploits. For instance, there is an increased amount of objects originating from a certain geographic location prone to exploits or from a certain IP address that has been identified as a malicious source. For parallel processing, operations associated with blocks 805-825 and 830-855 of
Upon receipt of an object under analysis, as set forth in block 800, the TDP system conducts a determination as to whether the static analysis should be conducted by the first static analysis logic (IPS logic) and/or the second static analysis logic (heuristic logic). According to one embodiment, as a default, the IPS logic is selected.
When selected, the IPS logic conducts exploit signature checks and/or vulnerability signature checks to determine whether characteristics of the object under analysis are indicative of an exploit (block 805). Upon determining that the characteristics of the object under analysis are indicative of an exploit, information associated with the suspect object and/or exploit (IPS-based results) is stored for subsequent access (blocks 810 and 815).
Although not shown, when determining that the suspect object has characteristics of a suspected exploit, the IPS logic may be configured to block the object from proceeding to the targeted client device, although blocking may be delayed until completion of the VM-based analysis. This blocking functionality may be adjusted by the network administrator based on the severity/type of suspected exploit, number of occurrences of this type of exploit within a prescribed time period, or the like. Furthermore, prior to performing further exploit analysis, as an optional feature identified by dashed lines in
Additionally or in the alternative, a second static analysis may be performed to determine whether characteristics of the object under analysis are indicative of an exploit (block 830). This determination may involve one or more heuristic checks being conducted in efforts to determine if the (i) the object has a certain level of correlation with one or more malicious identifiers or (ii) presence, absence or modification of any content associated with the object identifies a potential exploit. During such analysis, a score may be assigned to identify the likelihood of this object being an exploit (block 835).
In the event that the suspect object is tagged for VM-based analysis, which may be determined if the assigned score is greater than or equal to a prescribed threshold score, information associated with the suspect object and/or the potential exploit including the score (hereinafter referred to as “heuristic-based results”) may be stored for subsequent access by classification logic (blocks 840 and 845). Thereafter, the suspect object, optionally with tag_ID2, is provided to the dynamic analysis engine for subsequent analysis (blocks 850 and 855).
Regardless whether the static analysis is conducted by the IPS logic or the heuristic logic, the suspect object may be further analyzed by conducting VM-based analysis on the content associated with the suspect object, where behaviors of the virtual processing of the content by one or more VMs produces VM-based results (blocks 860 and 865). If the VM-based analysis fails to detect any exploit within content of the suspect object, a score may be assigned to denote that no exploit is detected and the VM-based results may be stored (blocks 870 and 875).
However, when the dynamic analysis engine verifies (during virtual processing of the content within the suspect object) that the suspect object constitutes an exploit, this “verified” exploit is assigned a score representative of the likelihood and/or threat level for the detected exploit(s). More specifically, during subsequent analysis of the content within the suspect object by the virtual execution logic, upon determining that the suspect object is an exploit (e.g., a certain probability that content within the suspect object constitutes an exploit is determined), a score representative of the likelihood and/or threat level for the detected exploit is assigned.
Thereafter, according to one embodiment of the disclosure, the IPS-based results along with the VM-based results are obtained and some or all of the information from the IPS-based results and the VM-based results may be prominently displayed (highlighted) as illustrated in blocks 880 and 885 and further described above.
Thereafter, the (highlighted) verified exploit information is uploaded into the database for storage and now accessible by display logic for rendering (blocks 890 and 895).
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.
This application is a divisional of U.S. patent application Ser. No. 14/228,073 filed Mar. 27, 2014, which claims the benefit of priority on U.S. Provisional Application No. 61/921,033, filed Dec. 26, 2013, the entire contents of both of which are incorporated by reference herein.
Number | Name | Date | Kind |
---|---|---|---|
4292580 | Ott et al. | Sep 1981 | A |
5175732 | Hendel et al. | Dec 1992 | A |
5440723 | Arnold et al. | Aug 1995 | A |
5490249 | Miller | Feb 1996 | A |
5657473 | Killean et al. | Aug 1997 | A |
5842002 | Schnurer et al. | Nov 1998 | A |
5978917 | Chi | Nov 1999 | A |
6088803 | Tso et al. | Jul 2000 | A |
6094677 | Capek et al. | Jul 2000 | A |
6108799 | Boulay et al. | Aug 2000 | A |
6118382 | Hibbs et al. | Sep 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 |
6417774 | Hibbs et al. | Jul 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 |
6700497 | Hibbs et al. | Mar 2004 | B2 |
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 |
6995665 | Appelt et al. | Feb 2006 | B2 |
7007107 | Ivchenko et al. | Feb 2006 | B1 |
7028179 | Anderson et al. | Apr 2006 | B2 |
7043757 | Hoefelmeyer et al. | May 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 |
7191335 | Maillard | Mar 2007 | B1 |
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 |
7437764 | Sobel et al. | Oct 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 |
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 |
7937761 | Bennett | May 2011 | B1 |
7949849 | Lowe et al. | May 2011 | B2 |
7996556 | Raghavan et al. | Aug 2011 | B2 |
7996836 | McCorkendale et al. | Aug 2011 | B1 |
7996904 | Chiueh et al. | Aug 2011 | B1 |
7996905 | Arnold et al. | Aug 2011 | B2 |
8006305 | Aziz | Aug 2011 | B2 |
8010667 | Zhang et al. | Aug 2011 | B2 |
8020206 | Hubbard et al. | Sep 2011 | B2 |
8028338 | Schneider et al. | Sep 2011 | B1 |
8042184 | Batenin | Oct 2011 | B1 |
8045094 | Teragawa | Oct 2011 | B2 |
8045458 | Alperovitch et al. | Oct 2011 | B2 |
8069484 | McMillan et al. | Nov 2011 | B2 |
8087086 | Lai et al. | Dec 2011 | B1 |
8171553 | Aziz et al. | May 2012 | B2 |
8176049 | Deninger et al. | May 2012 | B2 |
8176480 | Spertus | May 2012 | B1 |
8201246 | Wu et al. | Jun 2012 | B1 |
8204984 | Aziz et al. | Jun 2012 | B1 |
8214905 | Doukhvalov et al. | Jul 2012 | B1 |
8220055 | Kennedy | Jul 2012 | B1 |
8225288 | Miller et al. | Jul 2012 | B2 |
8225373 | Kraemer | Jul 2012 | B2 |
8233882 | Rogel | Jul 2012 | B2 |
8234640 | Fitzgerald et al. | Jul 2012 | B1 |
8234709 | Viljoen et al. | Jul 2012 | B2 |
8239944 | Nachenberg et al. | Aug 2012 | B1 |
8260914 | Ranjan | Sep 2012 | B1 |
8266091 | Gubin et al. | Sep 2012 | B1 |
8286251 | Eker et al. | Oct 2012 | B2 |
8291499 | Aziz et al. | Oct 2012 | B2 |
8307435 | Mann et al. | Nov 2012 | B1 |
8307443 | Wang et al. | Nov 2012 | B2 |
8312545 | Tuvell et al. | Nov 2012 | B2 |
8321936 | Green et al. | Nov 2012 | B1 |
8321941 | Tuvell et al. | Nov 2012 | B2 |
8332571 | Edwards, Sr. | Dec 2012 | B1 |
8365286 | Poston | Jan 2013 | B2 |
8365297 | Parshin et al. | Jan 2013 | B1 |
8370938 | Daswani et al. | Feb 2013 | B1 |
8370939 | Zaitsev et al. | Feb 2013 | B2 |
8375444 | Aziz et al. | Feb 2013 | B2 |
8381299 | Stolfo et al. | Feb 2013 | B2 |
8402529 | Green et al. | Mar 2013 | B1 |
8464340 | Ahn et al. | Jun 2013 | B2 |
8479174 | Chiriac | Jul 2013 | B2 |
8479276 | Vaystikh et al. | Jul 2013 | B1 |
8479291 | Bodke | Jul 2013 | B1 |
8510827 | Leake et al. | Aug 2013 | B1 |
8510828 | Guo et al. | Aug 2013 | B1 |
8510842 | Amit et al. | Aug 2013 | B2 |
8516478 | Edwards et al. | Aug 2013 | B1 |
8516590 | Ranadive et al. | Aug 2013 | B1 |
8516593 | Aziz | Aug 2013 | B2 |
8522348 | Chen et al. | Aug 2013 | B2 |
8528086 | Aziz | Sep 2013 | B1 |
8533824 | Hutton et al. | Sep 2013 | B2 |
8539582 | Aziz et al. | Sep 2013 | B1 |
8549638 | Aziz | Oct 2013 | B2 |
8555391 | Demir et al. | Oct 2013 | B1 |
8561177 | Aziz et al. | Oct 2013 | B1 |
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 |
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 |
8881282 | Aziz et al. | Nov 2014 | B1 |
8898788 | Aziz et al. | Nov 2014 | B1 |
8935779 | Manni et al. | Jan 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 |
9106694 | Aziz et al. | Aug 2015 | B2 |
9118715 | Staniford et al. | Aug 2015 | B2 |
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 |
20020116627 | Tarbotton et al. | Aug 2002 | A1 |
20020144156 | Copeland | Oct 2002 | A1 |
20020162015 | Tang | Oct 2002 | A1 |
20020166063 | Lachman et al. | Nov 2002 | A1 |
20020169952 | DiSanto et al. | Nov 2002 | A1 |
20020184528 | Shevenell et al. | Dec 2002 | A1 |
20020188887 | Largman et al. | Dec 2002 | A1 |
20020194490 | Halperin et al. | Dec 2002 | A1 |
20030074578 | Ford et al. | Apr 2003 | A1 |
20030084318 | Schertz | May 2003 | A1 |
20030101381 | Mateev et al. | May 2003 | A1 |
20030115483 | Liang | Jun 2003 | A1 |
20030188190 | Aaron et al. | Oct 2003 | A1 |
20030191957 | Hypponen et al. | Oct 2003 | A1 |
20030200460 | Morota et al. | Oct 2003 | A1 |
20030210789 | Farnham | Nov 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 |
20040015712 | Szor | Jan 2004 | A1 |
20040019832 | Arnold et al. | Jan 2004 | A1 |
20040047356 | Bauer | Mar 2004 | A1 |
20040083408 | Spiegel et al. | Apr 2004 | A1 |
20040088581 | Brawn et al. | May 2004 | A1 |
20040093513 | Cantrell et al. | May 2004 | A1 |
20040111531 | Staniford et al. | Jun 2004 | A1 |
20040117478 | Triulzi et al. | Jun 2004 | A1 |
20040117624 | Brandt et al. | Jun 2004 | A1 |
20040128355 | Chao et al. | Jul 2004 | A1 |
20040165588 | Pandya | Aug 2004 | A1 |
20040236963 | Danford et al. | Nov 2004 | A1 |
20040243349 | Greifeneder et al. | Dec 2004 | A1 |
20040249911 | Alkhatib et al. | Dec 2004 | A1 |
20040255161 | Cavanaugh | Dec 2004 | A1 |
20040268147 | Wiederin et al. | Dec 2004 | A1 |
20050005159 | Oliphant | Jan 2005 | A1 |
20050021740 | Bar et al. | Jan 2005 | A1 |
20050033960 | Vialen et al. | Feb 2005 | A1 |
20050033989 | Poletto et al. | Feb 2005 | A1 |
20050050148 | Mohammadioun et al. | Mar 2005 | A1 |
20050086523 | Zimmer et al. | Apr 2005 | A1 |
20050091513 | Mitomo et al. | Apr 2005 | A1 |
20050091533 | Omote et al. | Apr 2005 | A1 |
20050091652 | Ross et al. | Apr 2005 | A1 |
20050108562 | Khazan et al. | May 2005 | A1 |
20050114663 | Cornell et al. | May 2005 | A1 |
20050125195 | Brendel | Jun 2005 | A1 |
20050149726 | Joshi et al. | Jul 2005 | A1 |
20050157662 | Bingham et al. | Jul 2005 | A1 |
20050183143 | Anderholm et al. | Aug 2005 | A1 |
20050201297 | Peikari | Sep 2005 | A1 |
20050210533 | Copeland et al. | Sep 2005 | A1 |
20050238005 | Chen et al. | Oct 2005 | A1 |
20050240781 | Gassoway | Oct 2005 | A1 |
20050262562 | Gassoway | Nov 2005 | A1 |
20050265331 | Stolfo | Dec 2005 | A1 |
20050273856 | Huddleston | Dec 2005 | A1 |
20050283839 | Cowburn | Dec 2005 | A1 |
20060010324 | Appenzeller | Jan 2006 | 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 |
20060056632 | Kudelski | 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 | Gilde 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 |
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 |
20080028436 | Hannel | Jan 2008 | A1 |
20080028463 | Dagon et al. | Jan 2008 | A1 |
20080032556 | Schreier | Feb 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 |
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 |
20090044024 | Oberheide et al. | Feb 2009 | A1 |
20090044274 | Budko et al. | Feb 2009 | A1 |
20090064332 | Porras et al. | Mar 2009 | A1 |
20090077666 | Chen et al. | Mar 2009 | A1 |
20090083369 | Marmor | Mar 2009 | A1 |
20090083855 | Apap et al. | Mar 2009 | A1 |
20090089879 | Wang et al. | Apr 2009 | A1 |
20090094697 | Provos et al. | Apr 2009 | A1 |
20090113425 | Ports et al. | Apr 2009 | A1 |
20090125976 | Wassermann et al. | May 2009 | A1 |
20090126015 | Monastyrsky et al. | May 2009 | A1 |
20090126016 | Sobko et al. | May 2009 | A1 |
20090133125 | Choi et al. | May 2009 | A1 |
20090144823 | Lamastra et al. | Jun 2009 | A1 |
20090158430 | Borders | Jun 2009 | A1 |
20090172815 | Gu et al. | Jul 2009 | A1 |
20090187992 | Poston | Jul 2009 | A1 |
20090193293 | Stolfo et al. | Jul 2009 | A1 |
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 |
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 |
20100287613 | Singh 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 |
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 |
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 |
20110321166 | Capalik 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 |
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 |
20120278889 | El-Moussa | Nov 2012 | A1 |
20120297489 | Dequevy | Nov 2012 | A1 |
20120330801 | McDougal 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 |
20130263260 | Mahaffey et al. | Oct 2013 | A1 |
20130291109 | Staniford et al. | Oct 2013 | A1 |
20130298243 | Kumar et al. | Nov 2013 | A1 |
20140053260 | Gupta et al. | Feb 2014 | A1 |
20140053261 | Gupta et al. | Feb 2014 | A1 |
20140068775 | Ward et al. | Mar 2014 | A1 |
20140130158 | Wang et al. | May 2014 | A1 |
20140137180 | Lukacs et al. | May 2014 | A1 |
20140169762 | Ryu | Jun 2014 | A1 |
20140173739 | Ahuja et al. | Jun 2014 | A1 |
20140179360 | Jackson et al. | Jun 2014 | A1 |
20140328204 | Klotsche et al. | Nov 2014 | A1 |
20140337836 | Ismael | Nov 2014 | A1 |
20140351935 | Shao et al. | Nov 2014 | A1 |
20150096025 | Ismael | Apr 2015 | A1 |
Number | Date | Country |
---|---|---|
2106085 | Sep 2009 | EP |
2439806 | Jan 2008 | GB |
2490431 | Oct 2012 | GB |
02006928 | Jan 2002 | WO |
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 |
20131067505 | May 2013 | WO |
Entry |
---|
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. |
PCT/US2014/072292 filed Dec. 23, 2014 International Search Report and Written Opinion dated Feb. 23, 2015. |
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. 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). |
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. |
“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.jsp?reload=true&arnumbe- r=990073, (Dec. 7, 2013). |
Abdullah, et al., Visualizing Network Data for Intrusion Detection, 2005 IEEE Workshop on Information Assurance and Security, pp. 100-108. |
Adetoye, Adedayo , et al., “Network Intrusion Detection & Response System”, (“Adetoye”), (Sep. 2003). |
Adobe Systems Incorporated, “PDF 32000-1:2008, Document management—Portable document format—Part1:PDF 1.7”, First Edition, Jul. 1, 2008, 756 pages. |
AltaVista Advanced Search Results. “attack vector identifier”. Http://www.altavista.com/web/results?Itag=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?Itag=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-verlag 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. |
Cisco “Intrusion Prevention for the Cisco ASA 5500-x Series” Data Sheet (2012). |
Cisco, Configuring the Catalyst Switched Port Analyzer (SPAN) (“Cisco”), (1992-2003). |
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). |
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 Merike 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:https://web.archive.org/web/20121022220617/http://www.informationweek- .com/microsofts-honeymonkeys-show-patching-wi/167600716 [retrieved on Sep. 29, 2014]. |
Heng Yin et al, Panorama: Capturing System-Wide Information Flow for Malware Detection and Analysis, Research Showcase @ CMU, Carnegie Mellon University, 2007. |
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. |
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. |
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. |
Leading Colleges Select FireEye to Stop Malware-Related Data Breaches, FireEye Inc., 2009. |
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]. |
PCT/US2015/037245 filed Jun. 23, 2015 International Search Report and Written Opinion dated Sep. 17, 2015. |
U.S. Appl. No. 14/228,073, filed Mar. 27, 2015 Non-Final Office Action dated Jun. 15, 2015. |
U.S. Appl. No. 14/313,934, filed Jun. 24, 2014 Non-Final Office Action dated Sep. 30, 2015. |
U.S. Appl. No. 14/228,073, filed Mar. 27, 2014 Final Office Action dated Nov. 13, 2015. |
Number | Date | Country | |
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61921033 | Dec 2013 | US |
Number | Date | Country | |
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Parent | 14228073 | Mar 2014 | US |
Child | 14620055 | US |