Patent Grant
10515214
Embodiments of the present invention relate generally to malicious content detection. More particularly, embodiments of the invention relate to malicious content detection using intelligent static and dynamic analyses.
Malicious software, or malware for short, may include any program or file that is harmful by design to a computer. Malware includes computer viruses, worms, Trojan horses, adware, spyware, and any programming that gathers information about a computer or its user or otherwise operates without permission. The owners of the computers are often unaware that these programs have been added to their computers and are often similarly unaware of their function.
Malicious network content is a type of malware distributed over a network via websites, e.g., servers operating on a network according to a hypertext transfer protocol (HTTP) standard or other well-known standard. Malicious network content distributed in this manner may be actively downloaded and installed on a computer, without the approval or knowledge of its user, simply by the computer accessing the web site hosting the malicious network content (the “malicious web site”). Malicious network content may be embedded within objects associated with web pages hosted by the malicious web site. Malicious network content may also enter a computer upon receipt or opening of email. For example, email may contain an attachment, such as a PDF document, with embedded malicious executable programs. Furthermore, malicious content may exist in files contained in a computer memory or storage device, having infected those files through any of a variety of attack vectors.
Various processes and devices have been employed to prevent the problems associated with malicious content. For example, computers often run antivirus scanning software that scans a particular computer for viruses and other forms of malware. The scanning typically involves automatic detection of a match between content stored on the computer (or attached media) and a library or database of signatures of known malware. The scanning may be initiated manually or based on a schedule specified by a user or system administrator associated with the particular computer. Unfortunately, by the time malware is detected by the scanning software, some damage on the computer or loss of privacy may have already occurred, and the malware may have propagated from the infected computer to other computers. Additionally, it may take days or weeks for new signatures to be manually created, the scanning signature library updated and received for use by the scanning software, and the new signatures employed in new scans.
Moreover, anti-virus scanning utilities may have limited effectiveness to protect against all exploits by polymorphic malware. Polymorphic malware has the capability to mutate to defeat the signature match process while keeping its original malicious capabilities intact. Signatures generated to identify one form of a polymorphic virus may not match against a mutated form. Thus polymorphic malware is often referred to as a family of virus rather than a single virus, and improved anti-virus techniques to identify such malware families is desirable.
Another type of malware detection solution employs virtual environments to replay content within a sandbox established by virtual machines (VMs). Such solutions monitor the behavior of content during execution to detect anomalies that may signal the presence of malware. One such system offered by FireEye®, Inc., the assignee of the present patent application, employs a two-phase malware detection approach to detect malware contained in network traffic monitored in real-time. In a first or “static” phase, a heuristic is applied to network traffic to identify and filter packets that appear suspicious in that they exhibit characteristics associated with malware. In a second or “dynamic” phase, the suspicious packets (and typically only the suspicious packets) are replayed within one or more virtual machines. For example, if a user is trying to download a file over a network, the file is extracted from the network traffic and analyzed in the virtual machine. The results of the analysis aids in determining whether the file is malicious. The two-phase malware detection solution may detect numerous types of malware and, even malware missed by other commercially available approaches. Through verification, the two-phase malware detection solution may also achieve a significant reduction of false positives relative to such other commercially available approaches. Dealing with false positives in malware detection may needlessly slow or interfere with download of network content or receipt of email, for example. This two-phase approach has even proven successful against many types of polymorphic malware and other forms of advanced persistent threats.
Typically, the static phase and the dynamic phase are performed in sequence, in which a static analysis is performed followed by a dynamic analysis, to generate separate scores with limited or no influence from each other. The scores are then used to determine the final malware score of the content for content classification. The static or dynamic phase may be performed in an operating environment that may not be correct and/or necessary. For example, a dynamic analysis may be performed on the content for specific types and/or versions of operating systems and/or applications executing within a virtual environment, even if a static analysis reveals that the content is intended for a particular version of a particular type of operating system and/or application. As a result, drawbacks of known two-phase malware detection solutions include a certain inflexibility and inefficiency in performing the analysis.
Embodiments of the invention are illustrated by way of example and not limitation in the figures of the accompanying drawings in which like references indicate similar elements.
Various embodiments and aspects of the invention will be described with reference to details discussed below, and the accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative of the invention and are not to be construed as limiting the invention. Numerous specific details are described to provide a thorough understanding of various embodiments of the present invention. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments of the present inventions.
Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in conjunction with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification do not necessarily all refer to the same embodiment.
Techniques for malware detection using intelligent static analysis and dynamic analysis are described herein. According to one embodiment, a malware detection system includes, but is not limited to, a first analysis module (e.g., a static analysis module), a second analysis module (e.g., a dynamic analysis module), a malware classifier, and a controller. In response to receiving a specimen (e.g., a content item such as a data object or file to be analyzed) for malware detection, the controller determines an analysis plan for analyzing whether the specimen should be classified as malware. The analysis plan identifies at least a first analysis and possibly plural analyzes, along with an order and, in some embodiments, an analysis “protocol” for performing the analysis. For example, the analysis plan may specify a first analysis and a second analysis to be performed, which may be a static analysis and/or a dynamic analysis, and the order in which they are to be performed. In some embodiments, the analysis plan may also specify an analysis protocol and parameters, such as a specific operating environment to be provided or specific behaviors to be monitored in the dynamic analysis, specific types of static analyses, or specific characteristics to be checked, verified or examined via static analysis.
Where the analysis plan specifies two analyses, e.g., a first and second analysis, the first analysis may be performed by the first analysis module according to the analysis plan, for example, to identify one or more suspicious indicators and one or more characteristics related to processing of the specimen. A second analysis may be performed by the second analysis module in accordance with the analysis plan on the specimen, for example, to identify one or more unexpected behaviors that include one or more processing or communications anomalies. The results of the first and second analyses are provided to the classifier. The classifier is to classify the specimen based on the identified suspicious indicators and the anomalies. The analysis plan and all the information generated from the first and second analysis and the classification are stored in a persistent storage (which may be located locally and/or remotely), including the suspicious indicators, characteristics, information describing the unexpected and/or expected behaviors, as well as the specimen itself and the metadata describing the circumstances surrounding the specimen (e.g., email or Web information through which the specimen was received).
The controller uses the stored information to determine what, if any, additional analysis or analyses should be performed, and, often, what protocols should be followed during the subsequent testing. The sequence order of the analyses involved may be determined by the controller as part of the analysis plan, or update to the analysis plan. In one embodiment, the controller monitors or receives feedback from the analysis modules and the classifier, and may modify or adjust the analysis plan based on the results of a prior analysis and classification, including configuring an additional analysis between or after the first and second analysis or modifying a procedure or operating environment of the next analysis in the analysis plan.
Accordingly, the first analysis may be performed prior to the second analysis, where the second analysis may be performed based in part, for example, on the information or results generated from the first analysis, such as suspicious indicators and characteristics. Alternatively, the second analysis may be performed prior to the first analysis, where the first analysis may be performed based in part, for example, on at least one of the anomalies identified during the second analysis. Some embodiments of the invention may improve over the known two-phase malware detection solutions by providing a third type of analysis involving emulation as a simpler, more time efficient method of analysis than the dynamic analysis involving a virtual machine, either in lieu of or in addition to dynamic analysis. Such three-phase malware detection solutions provide additional options for the controller in conducting analysis.
As a result, embodiments of the invention may perform malware detection with greater flexibility in the conduct of the analysis, and realize greater efficiencies with improved efficacy in detecting malware than in known two-phase malware detection solutions.
With reference now to the drawings,
For instance, the specimen may include a content item that is created (i.e. dropped) by the original specimen during virtual (or emulated) processing of the original specimen. This content item may include, but is not limited or restricted a second (new) file, a data object (e.g., document, library, etc.), executable, or a process. For content that is downloaded from a public network (e.g., a Web download) and is under dynamic analysis (or emulation), the specimen may include different types of data objects than content uploaded from a file server, such as a universal resource locator (URL), a domain name, or an universal resource identifier (URI) that is called during analysis of the Web download.
In one embodiment, malicious content detection system 101 includes, but is not limited to, static analysis module 102 (also referred to as static analysis logic or static analyzer), dynamic analysis module 103 (also referred to as dynamic analysis logic or dynamic analyzer), malware classifier 105, controller 106 (also referred to as control logic), and intelligence store or database 110. Static analysis module 102 is to perform a static analysis on the specimen 180 without executing or playing the specimen. A static analysis may include a comparison of content of the specimen 180 to security-based content such as a signature or other data pattern, a blacklist, a whitelist, or the like. Hence, this comparison may feature a signature match, protocol semantics anomalies check, source reputation check, malware source blacklist or whitelist checking, and/or emulation. Dynamic analysis module 103 is to perform a dynamic analysis on the specimen, including monitoring behaviors of the specimen 180 during its virtual execution to detect any unexpected behaviors having one or more anomalies. Malware classifier 105 is to classify whether the specimen is likely malicious based on the results of the static analysis and dynamic analysis, and other information such as information stored in the intelligence store 110. Controller 106 is to coordinate the operations of the static analysis module 102, the dynamic analysis module 103, and the classifier 105, including controlling the processing flows amongst them via an analysis plan or a feedback received from any of the static and dynamic analysis modules 102-103 and classifier 105. The controller 106 is to determine in an analysis plan whether one or both of the analysis modules 102-103 should be involved, the order of the analysis modules 103-103 involved (which may be in series or in parallel), whether additional analysis is needed based on the feedback from the classifier 105 and the intelligence information stored in the intelligence store 110. Effectively, controller 106 determines an analysis plan or roadmap for static analysis module 102, dynamic analysis module 103, and malware classifier 105. Although two analysis modules are shown in
According to one embodiment, the information stored in the intelligence store 110 (e.g., a persistent database) is accessible and used by each of the components of the malware detection system (e.g., static analysis module 102, dynamic analysis module 103, malware classifier 105, and controller 106) during all processing stages of malware detection processes. Each of the components may utilize the information stored in the intelligence store 110 during their respective processes. Each of the components may generate and store further intelligence information in the intelligence store 110 during their respective processes, which may be utilized in subsequent operations. The intelligence information stored in the intelligence store 110 includes a variety of information obtained during the current malware detection session and prior malware detection sessions (if any), and/or other information received or updated from other information sources, such as external analysis data and control server 120 in the cloud (e.g., over the Internet). The intelligence information may include metadata of the specimen, information concerning the circumstances surrounding the specimen (e.g., environment in which the specimen is received such as email information, Web information, or information associated with a data object, file or process created during virtual processing or emulation of another specimen), information observed or learned during the operations of each of the components of the malware detection system, and/or other information obtained from other malware detection systems with respect to the same or similar specimen. The specimen itself may also be cached in the intelligence store 110.
At least some of the components such as controller 106 may be equipped with a logger to log all the events or activities occurred during the processes of the respective components. The logged information may also be stored in intelligence store 110 and accessible by all components. As a result, each of the components of the malware detection system has all the intelligence information available from the intelligence store 110 during the corresponding stage of processes and it can perform a more comprehensive and accurate analysis in view of all the intelligence information generated from the past and current malware detection sessions. Since all components share all of the intelligence information, they effectively are on the “same page,” and communicate with one another (e.g., feedback), which enables each component to make intelligent decisions to improve the efficiency and accuracy of the malware detection. The information stored in intelligence store 110 may be stored in a persistent storage device (e.g., hard drive or flash memory device) and loaded in the system memory during the malware detection. The information stored in intelligence store 110 may be synchronized from time to time with a central management server such as server 120 for further analysis (e.g., offline analysis) and for sharing of information with other malicious content detection systems. For example, controller 106 may determine that the specimen has characteristics, identifiers, or behaviors that merit sending the specimen outside of the customer's network or sub-network (e.g., to a remote or centralized location, which may provide cloud-based subscription services) for additional (e.g., factory) processing, should the customer opt-in to this option.
In response to receiving a specimen for malware detection, the controller 106 determines an analysis plan for analyzing whether the specimen should be classified as malware. The specimen may be recently captured or received from a remote source or alternatively, it can be the same specimen that has been processed during a previous iteration of malware detection processes. Controller 106 may automatically determine, without user intervention, a next analysis based on the received specimen, the results of a prior analysis, and/or external factors such as an update of security-based content. In some cases, the results of the prior analysis may prompt the controller to alter the analysis plan to conduct one or more analyses on a particular result that is now considered to be the “specimen” under analysis. Controller 106 records this analysis decision in the analysis plan and the results of all analysis are stored in the memory in association with a specimen identifier identifying the received specimen. A specimen identifier may be a filename or other identifying information, where the specimen identifier may identify relatedness between the specimen currently under analysis and a (parent/original) specimen that, during prior analysis created the specimen under analysis during prior analysis of that specimen.
The analysis plan identifies at least one analysis to be performed, for example, for purposes of the following discussion, a first and second analysis, each of which may be a static analysis and/or a dynamic analysis. A first analysis (e.g., static analysis) is then performed by the first analysis module (e.g., static analysis module 102) according to the analysis plan to identify one or more suspicious indicators and one or more characteristics related to processing of the specimen. In addition, certain non-suspicious indicators (e.g., predefined data patterns) may also be tracked. A second analysis (e.g., dynamic analysis) is performed by the second analysis module (e.g., dynamic analysis module 103) in accordance with the analysis plan on the specimen to identify one or more unexpected behaviors that include one or more processing or communications anomalies. Similarly, certain expected behaviors may also be recorded. The classifier 105 is to classify the specimen based on the identified suspicious indicators and the anomalies. The analysis plan and all the information generated from the first and second analysis and the classification are stored in a persistent storage, such as intelligence store 110 or external server 120.
The first analysis may be performed prior to the second analysis, where the second analysis may be performed based in part on the information or results generated from the first analysis, such as suspicious indicators and characteristics. Alternatively, the second analysis may be performed prior to the first analysis, where the first analysis may be performed based in part on at least one of the anomalies identified during the second analysis. Furthermore, controller 106 may perform an initial analysis or scanning on the received specimen and may decide to dispatch the specimen for both analysis modules 102-103 for static and dynamic analyses in parallel. In one embodiment, the controller 106 monitors or receives feedback from at least one of the analysis modules 102-103 and the classifier 105. Controller 106 may modify or adjust the analysis plan based on the results of the analysis and the classification, including configuring and initiating an additional analysis (e.g., static or dynamic analysis) between or after the first and second analysis or modifying a procedure (e.g., protocol) or environment settings of the next analysis in the analysis plan. Controller 106 may further specify the order of multiple analyses listed in the analysis plan. The analysis plan may be updated and maintained in the intelligence store 110.
In one embodiment, after performing a static analysis before performing a dynamic analysis, controller 106 may alter the analysis plan based on the result of the static analysis, as well as other information obtained from the intelligence store 110. Controller 106 may decide to perform an additional analysis, e.g., by adding processing of the specimen in an emulation analysis module to unpack (e.g., decompress, decrypt or a combination thereof) an object and then another static analysis on the unpacked object. The dynamic analysis is then performed pursuant to the analysis plan based in part on the results of the inserted static analysis. Thus, a result of one analysis or operation may provide an influence to a subsequent analysis or operation. The influence may be any information or data that affects or alters the decision making regarding a subsequent analysis or the conduct or operation of the malware detection process during that subsequent analysis. For example, the influence generated by a static analysis on a subsequent dynamic analysis may include the runtime environment used by the subsequent dynamic analysis, including a type of operating system and its version, type of specimen (e.g., executable, PDF, Web, WORD), applications involved (e.g., browser), etc., the length of time to conduct a dynamic analysis on the specimen, or the type of behaviors to be monitored, or the type or location of monitors (or monitor instrumentation) to deploy. These are examples of the analysis protocol and parameters referred to above.
According to one embodiment, controller 106 may automatically modify, without user intervention, the priorities of the specimens to be analyzed in the analysis plan based on the information observed (from the intelligence store) at the point in time. Initially, for example, when the specimens are received for malware detection, controller 106 may perform an initial analysis on the specimens, associate a priority with each of the specimens, and set an order of the analyses to be performed in an analysis plan. After a first analysis (e.g., static analysis), controller 106 may modify the priorities of the specimens and/or the order of the analyses in the analysis plan based on the result of the first analysis. Controller 106 may further configure the time or analysis schedule for each of the analyses to be performed in the analysis plan. The time or analysis schedule information may also be stored in the analysis plan. Controller 106 then dispatches the specimens to be analyzed according to the analysis schedule or time specified in the analysis plan.
According to another embodiment, after a static analysis has been performed, based on the result of the static analysis, controller 106 may select a runtime environment of a dynamic analysis that is supposed to be performed after the static analysis. For example, controller 106 may determine an operating system and version thereof, an application and version thereof for the virtual environment of the dynamic analysis. Controller 106 may further select an initial state from which the application will be run based on the result of the static analysis. Controller 106 may alter the analysis plan to reflect such changes.
According to another embodiment, any results (e.g., events), activities, and/or decision makings of all of the components may be recorded (for example, by controller 106, or by the individual components themselves) in the analysis plan or an analysis log, which may be stored in database 110 and/or external storage 120. The recorded information may be stored in database 110, which may be indexed based on identifiers of the specimen. Controller 106 may determine a next analysis based on prior analysis results and dispatch the specimen to one or both of analysis modules 102-103 via a virtual switch, where the virtual switch is operated based on certain events or conditions maintained by the intelligence store 110. Controller 106 may also determine the length of a dynamic analysis and specific software to run therein, including an operating system, applications, libraries, plugins, and versions thereof based on the specimen or one or more prior analyses. Controller 106 may continue directing a further analysis or terminate the current analysis after a period of time, which may be determined based on a number of pending specimens.
In one embodiment, a static analysis may be performed in view of the intelligence information stored in intelligence store 110. A static analysis may include signature match, protocol semantics anomalies check, source reputation check and/or emulation. Static analysis module 102 further extracts information from the specimen that describes the specimen. The extracted information is stored in intelligence store 110. Static analysis module 102 may further generate intelligence information during the static analysis and store the intelligence information in intelligence store 110. Static analysis result 111 may also be stored in intelligence store 110. Static analysis module 102 may further perform an analysis based on a set of heuristics and to generate a static score representing the likelihood that a specimen is malicious based on the static analysis. The static score may be a measure of probability of malware and used in part by malware classifier 105 to classify the specimen.
In one embodiment, the specimen is statically inspected by static analysis module 102 for various attributes and “features.” These features are intended to be signals to both goodness and badness of the specimen. For example if a file contains a Microsoft® WORD® icon as its own display icon, this may “look” suspicious since that is a common malware technique to trick a user into opening the file. During the subsequent dynamic analysis, the file is dynamically analyzed by dynamic analysis module 103 for various behavioral actions, and it may be discovered that the file may not be opened by Microsoft WORD and/or may perform activities (e.g., behaviors) that are not expected of a WORD document. The “intelligent” aspect of the dynamic analysis is that the information from the static analysis can be used to help or influence the dynamic analysis. Such information may be stored in intelligence store 110.
Dynamic analysis module 103 is configured to monitor the behaviors of the specimen in an operating environment (e.g., virtual machine), generating a dynamic analysis result 112. Dynamic analysis result 112 may include information describing or indicating the unexpected and/or expected behaviors observed during the dynamic analysis. Dynamic analysis result 112 may be stored in the intelligence store 110 as well. The dynamic analysis may be configured and performed in view of the intelligence information obtained from the intelligence store 110. Dynamic analysis module 103 may further generate and store further intelligence information in intelligence store 110 during the dynamic analysis. Dynamic analysis module 103 may further generate a dynamic score representing the likelihood that specimen is malicious based on the dynamic analysis, which may be in a form of a measure of probability. Static analysis result 111 and dynamic analysis 112 are used by malware classifier 105 to determine, in view of the intelligence information obtained from the intelligence store 110, a malware classification indicator 109 that indicates whether the specimen is malicious, non-malicious, or uncertain, which may also be stored in the intelligence store 110. Malware classification indicator 109 may be in a form of confidence score.
It is contemplated that, during analysis of a content item (original specimen) and one or more content items created (i.e. dropped) during dynamic analysis of the original specimen (hereinafter “dropped specimen(s)”), the malware classification indicator 109 may comprise a resultant score that is produced by the consolidation of scores associated with the original specimen and the dropped specimen(s), where the controller 106 treats the dropped specimen as an extension (i.e. part) of the original specimen. A dropped specimen may be determined based on the specimen identifier which may identify relatedness between specimens as described above. Alternatively, the malware classification indicator 109 may be generated on a per specimen basis, where the controller 106 treats the dropped specimen as a completely different specimen from the original (parent) specimen. It is further contemplated that a malware classification indicator of a dropped specimen may be assigned an additional score value, especially where certain types of dropped specimens are more likely classified as malware.
Malware classification indicator 109 is fed back to controller 106 to determine whether the malware classification indicator 109 is sufficient or conclusive enough to classify the specimen. If so, controller 106 may terminate the analysis and reporting module 108 is invoked to report whether the specimen is indeed malware or non-malware. In the event the specimen is malware, a malware signature or malware identifier may also be generated for future detection. In the event the malware classification indicator 109 indicates the specimen is uncertain, controller 106 may configure additional analysis to be performed. Controller may further determine certain parameters or environment settings for the additional analysis based on the intelligence information obtained from the intelligence store 110. Controller 106 may further extend the clock time based on the results being obtained in the dynamic analysis or launch another dynamic analysis in response to those results.
According to one embodiment, the static analysis and dynamic analysis performed by static analysis module 102 and dynamic analysis module 103 may be performed in sequence (configured via an analysis plan) in which an analysis result of an analysis (e.g., static analysis) may be utilized, for example, via intelligence store 110, by a subsequent analysis (e.g., dynamic analysis) to improve the efficiency and accuracy of the subsequent analysis. In one embodiment, when a specimen is received, for example, via a network tap, for malware detection, controller 106 determines which of the static analysis and dynamic analysis should be performed first. For certain types of content (e.g., portable document format (PDF), a dynamic-linked library (DLL)), a static analysis may be performed first and a dynamic analysis may then be performed. For other types of content (e.g., Web page or an executable), a dynamic analysis may be performed prior to a static analysis.
According to one embodiment, an analysis module generates further intelligent information concerning the content in question, such as a type of content, and/or an operating system and its version in which the content is intended to be executed. Such intelligent information is utilized by another analysis module to perform a subsequent analysis in a manner specifically tailored to the content in question. For example, the result of a static analysis can be used to configure an operating environment that is specifically tailored to the content for the dynamic analysis.
According to one embodiment, if controller 106 determines that there is a discrepancy between intelligent information provided by static analysis module 102 and dynamic analysis module 103 (which may be stored in intelligence store 110 or received via an application programming interface or API), it may configure additional analysis to be performed. For example, a first static analysis may reveal a first set of features of a specimen in question. However, after a first dynamic analysis on the same specimen is performed, it may reveal a second feature that has not been detected by the first static analysis. The second feature may have been intentionally hidden by a developer or a provider of the specimen (e.g., a malware author). Such a discrepancy may be determined by controller 106 and/or classifier 105 as a red flag, for example, based on prior statistics collected over a period of time. In such a situation, controller 106 may determine that a further analysis is needed. As a result, a second static analysis may be performed on the specimen in view of the second feature discovered by the first dynamic analysis. The second static analysis may further require a second dynamic analysis to follow.
According to one embodiment, in addition to determining suspicious indicators, static analysis module 102 may further capture non-suspicious indicators, which may be user configurable. The non-suspicious indicators may also be stored in the intelligence store 110. Similarly, in addition to capturing the unexpected behaviors, dynamic analysis module 103 may further record expected behaviors and store the recorded information in the intelligence store 110. For example, if a specimen goes out-of-its-way to look normal during a static analysis, producing non-suspicious indicators, any unexpected behavior detected during a subsequent dynamic analysis may be considered with more weights, since it constitutes discrepancy between the two analyses.
According to one embodiment, in addition to the analysis results 111-112, other information generated by other components (e.g., information stored in the intelligence store 110) may also be presented or available to malware classifier 105. For example, the specimen itself, as well as its environment (e.g., associated email, Web information, and/or related file(s)) may also be presented or available to malware classifier 105.
According to one embodiment, controller 106 may determine, in the middle of a malware detection session based on the information observed, that the current analysis plan was not configured correctly. Controller 106 may decide to abort or abandon the current analysis plan completely and initiate another analysis plan or alternatively, take some correction or recovery actions before continue the current analysis plan. Furthermore, controller 106 may take into account the work load of the malware detection system and may decide to offload the analyses to an offline facility for malware analyses.
According to one embodiment, the number of specimens or the network, email, file work load of the system may also be provided to the malware classifier 105. The type of deployment may also be provided to the malware classifier 105. The controller 106 may determine that specimen has characteristic, identifiers, or behavior that merit sending the specimen outside of the customer's network for additional factory processing, should the customer opt-in to this option.
Note that the configuration of malware detection system 101 is described and shown in
Note that an analysis plan may be a formal analysis plan and alternatively, the analysis plan may simply map to some specimen identifiers, one or more analyses, and/or information related to the specimen and the analyses. An analysis plan can be configured or programmed using a variety of programming languages such as extensible markup language (XML) or other scripting languages. The analysis plan may be updatable via a user interface or an API.
Emulation analysis module 104 is configured to emulate operations of an object and monitor for anomalous behavior. The monitoring may be accomplished by “hooking” certain functions associated with that object (e.g., one or more APIs, etc.), and controlling what data is specifically returned in response to corresponding function calls (e.g., force return of an application version number different than its actual number). After receipt of the returned data, operations by the object are monitored. For instance, the output from the object may be analyzed to determine if a portion of the output matches any of the malware identifiers.
According to one embodiment, identifier matching logic 203 is to match the identifying information of the specimen with a list of identifiers identifying a set of known malware (e.g., black list) and a set of known non-malware (e.g., white list). The list of identifiers may be collected based on prior malware detection and periodically updated from a centralized server in the cloud. If the specimen is identified as one of the matched identifiers in the list, the specimen can be classified right away as either malware or non-malware, without having to perform a further analysis. The identifiers or identifying information may include URLs, observed behaviors, characteristics, features, hash of a malicious object, reputation indicators from third-party reputation service as applied to known malicious sources (e.g., URLs, domain names).
According to one embodiment, analysis logic 204 includes an analysis selector 251, a plan generator 252, and dispatch logic 253. Analysis selector 251 is to select which of the static analysis, dynamic analysis, emulation analysis and classification should be performed. Plan generator 252 is to configure and generate an analysis plan having one or more selected analyses and/or emulation therein. Plan generator 252 is to decide which one or both or how many of a static analysis and dynamic analysis (and emulation analysis, depending on the embodiment) are needed, their sequence order of such analyses to be performed, and other protocol and parameters of these analyses. Plan generator 252 may decide based on a set of rules (not shown), which may be user configurable locally or remotely via a user interface (e.g., command line interface or CLI) and from time to time updated from an external source. Dispatch logic 253 may configure a VM with a set of parameters based on the information provided by object capturing logic 201 and/or preliminary filtering logic 202, based on the customer's specific requirements, or results of prior analysis or analyses. Dispatch logic 253 then dispatches the analysis tasks to any of the analysis modules and classifier in accordance with the analysis plan. All of the information generated from object capturing logic 201, preliminary filtering logic 202, identifier matching logic 203, and dispatch logic 253 may become part of analysis plan 210 stored in intelligence store 110.
In one embodiment, intelligence store 110 may include static analysis data store 403 to store any data generated from a static analysis (which may include the static analysis result), dynamic analysis store 404 to store any data generated from a dynamic analysis (which may include the dynamic analysis result), emulation analysis store 406 (which may include the emulation analysis result), and a context store 405 storing any context information, for example, generated from controller 106. Models 402 may be periodically trained and updated, for example, from an external centralized server.
The techniques described above can be applied in a variety of scenarios. For example, in the event that the specimen is a PDF file, static analysis module 102 is configured to determine and generate additional intelligence information in a form of metadata concerning the specimen. The context may include a type of the specimen, a type, version, and/or language of an operating system in which the specimen is intended to be executed, other software components (e.g., a specific version of a PDF reader), and other possible environment settings (e.g., an amount of a memory, a type of a processor, date and time of the operating environment), etc. Based on the context, controller 106 determines or configures an operating environment in which the specimen can be dynamically analyzed by dynamic analysis module 103. In one embodiment, a scheduler (which may be implemented as part of controller 106) provisions and configures a virtual machine (VM) from a pool of VMs based in part on the information obtained from context. In this example, an operating system of the VM may be configured or installed as the same as or similar to the one identified by the context, as well as other software components, virtual memory and processor, etc. Thus, the VM would be configured to be specifically tailored to the targeted operating environment in which the specimen is intended to be processed. As a result, although it can, dynamic analysis module 103 does not have to analyze the specimen in other unrelated or unintended operating environments or using other unrelated or unintended software components, which may significantly improve the efficiency and accuracy of the dynamic analysis.
In addition to weaponized documents, such as a PDF document, the specimen may be a malware type of document, such as a dynamically-link library (DLL). For example, when the specimen in the form of a DLL is received, a static analysis is performed on the content file by static analysis module 102. The static analysis may reveal certain specific processes that are related to the DLL in question. According to one embodiment, when a dynamic analysis is performed, those specific processes, instead of general-purpose processes, may be performed to determine whether the DLL is malicious. As a result, the speed and accuracy of the dynamic analysis can be greatly improved. Further, a static analysis may reveal only certain exported functions existed in the DLL and a dynamic analysis can focus on those existing exported functions without having to test other non-existing functions.
As mentioned above, under certain situations, a dynamic analysis may be performed prior to a static analysis, where the result of the dynamic analysis may be used by the static analysis. For example, if the specimen is a packed DLL file or an executable binary, the static analysis may not be able to fully examine the content based on heuristics. In this situation, a dynamic analysis can unpack the file during processing of the file to reveal other software components (e.g., network stack or other specific libraries). Based on the result of the dynamic analysis, the static analysis can be performed on the unpacked files using related heuristics.
Note that the specific sequence order of operations as shown in
In another example, referring back to
In a further example, a first static analysis performed on a specimen determines that the specimen is a packed file. In response, the controller configures a dynamic analysis or emulation performed on the specimen, which may unpack the file. A second static analysis may be performed on the unpacked file. The second static analysis may detect the evasion (also referred to as anti-detection defense or anti-analysis defense) such as virtual machine evasion. Based in part on the detected evasion, a classifier may classify the specimen as malware.
The host OS may host a VM monitor or manager (VMM), also referred to as a hypervisor, for managing or monitoring VMs. VM 752 may be hosted by a guest OS. The host OS and the guest OS may be the same type of operating systems or different types of operating systems (e.g., Windows™, Linux™, Unix™, Mac OS™, iOS™, etc.) or different versions thereof. A VM is a simulation of a machine (abstract or real) that is usually different from the target machine (where it is being simulated on). Virtual machines may be based on specifications of a hypothetical computer or emulate the computer architecture and functions of a real world computer. A virtual machine referred to herein can be any type of virtual machine, such as, for example, hardware emulation, full virtualization, para-virtualization, and operating system-level virtualization virtual machines.
The Host OS further hosts or provides an operating environment to analyzer 751, including static analysis module 102, malware classifier 105, controller 106, and emulation analysis module 104, as described above. According to one embodiment, when a specimen 706 is received for a dynamic analysis (as opposed to a static analysis performed by static analysis module 102), a scheduler 740 is configured to identify and select, or configure a VM, in this example VM 752, from a VM pool 703 that has been configured to closely simulate a target operating environment (e.g., particular version of an OS with particular versions of certain software installed therein) in which specimen 706 is to be analyzed. In one embodiment, based on an analysis result performed by static analysis module 102, a VM such as VM 752 is configured and scheduled by scheduler 740 specifically tailored to an operating environment 710 in which specimen 706 is intended for execution. The scheduler 740 then launches VM 752 in which dynamic analysis module 103 is running within VM 752 and configured to monitor activities and behavior of specimen 706. An emulation analysis may be performed by emulation analysis module 104 as described above. Furthermore, the analysis results generated by static analysis module 102 and/or dynamic analysis module 103 may be stored in corresponding VM disk files 760, for example, as part of intelligence store 110.
The malicious content detection system 850 is illustrated with a server device 810 and a client device 830, each coupled for communication via a communication network 820. In various embodiments, there may be multiple server devices and multiple client devices sending and receiving data to/from each other, and the same device can serve as either a server or a client in separate communication sessions. Although
Note that throughout this application, network content is utilized as an example of a specimen or specimens for malicious content detection purposes; however, other types of content can also be applied. Network content may include any data transmitted over a network (i.e., network data). Network data may include text, software, images, audio, or other digital data. An example of network content includes web content, or any network data that may be transmitted using a Hypertext Transfer Protocol (HTTP), Hypertext Markup Language (HTML) protocol, or be transmitted in a manner suitable for display on a Web browser software application. Another example of network content includes email messages, which may be transmitted using an email protocol such as Simple Mail Transfer Protocol (SMTP), Post Office Protocol version 3 (POP3), or Internet Message Access Protocol (IMAP4). A further example of network content includes Instant Messages, which may be transmitted using an Instant Messaging protocol such as Session Initiation Protocol (SIP) or Extensible Messaging and Presence Protocol (XMPP). In addition, network content may include any network data that is transferred using other data transfer protocols, such as File Transfer Protocol (FTP).
The malicious network content detection system 850 may monitor exchanges of network content (e.g., Web content) in real-time rather than intercepting and holding the network content until such time as it can determine whether the network content includes malicious network content. The malicious network content detection system 850 may be configured to inspect exchanges of network content over the communication network 820, identify suspicious network content, and analyze the suspicious network content using a virtual machine to detect malicious network content. In this way, the malicious network content detection system 850 may be computationally efficient and scalable as data traffic volume and the number of computing devices communicating over the communication network 820 increases. Therefore, the malicious network content detection system 850 may not become a bottleneck in the computer network system 800.
The communication network 820 may include a public computer network such as the Internet, in which case a firewall 825 may be interposed between the communication network 820 and the client device 830. Alternatively, the communication network may be a private computer network such as a wireless telecommunication network, wide area network, or local area network, or a combination of networks. Though the communication network 820 may include any type of network and be used to communicate different types of data, communications of web data may be discussed below for purposes of example.
The malicious network content detection system 850 is shown as being coupled with the network 820 by a network interface or tap 840 (e.g., a data/packet capturing device). The network tap 840 may include a digital network tap configured to monitor network data and provide a copy of the network data to the malicious network content detection system 850. Network data may comprise signals and data that are transmitted over the communication network 820 including data flows from the server device 810 to the client device 830. In one example, the network tap 840 monitors and copies the network data without an appreciable decline in performance of the server device 810, the client device 830, or the communication network 820. The network tap 840 may copy any portion of the network data, for example, any number of data packets from the network data. In embodiments where the malicious content detection system 850 is implemented as a dedicated appliance or a dedicated computer system, the network tap 840 may include an assembly integrated into the appliance or computer system that includes network ports, network interface card and related logic (not shown) for connecting to the communication network 820 to non-disruptively “tap” traffic thereon and provide a copy of the traffic to the heuristic module 860. In other embodiments, the network tap 840 can be integrated into a firewall, router, switch or other network device (not shown) or can be a standalone component, such as an appropriate commercially available network tap. In virtual environments, a virtual tap (vTAP) can be used to copy traffic from virtual networks.
The network tap 840 may also capture metadata from the network data. The metadata may be associated with the server device 810 and/or the client device 830. For example, the metadata may identify the server device 810 and/or the client device 830. In some embodiments, the server device 810 transmits metadata which is captured by the tap 840. In other embodiments, a heuristic module 860 (described herein) may determine the server device 810 and the client device 830 by analyzing data packets within the network data in order to generate the metadata. The term, “content,” as used herein may be construed to include the intercepted network data and/or the metadata unless the context requires otherwise.
The malicious network content detection system 850 may include a static analysis module 860, a heuristics database (not shown), a scheduler 870, a virtual machine pool 880, a dynamic analysis module 882, an emulator (not shown), and a reporting module 884. In some embodiments, the network tap 840 may be contained within the malicious network content detection system 850. The controller 106 is to coordinate, via an analysis plan, at least one of a static analysis, a dynamic analysis, and an emulation, in which one process may utilize intelligent information produced by another process and stored in intelligence store 110. Classifier 105 is to classify whether a particular specimen should be classified as malware based on the static analysis, dynamic analysis, and/or the emulation. In addition, controller 106 further examines the results of a static analysis and a dynamic analysis to determine whether a further static analysis, dynamic analysis, or both are needed. If so, controller 106 configures a new analysis plan or modifies an existing analysis plan to include at least one additional analysis to be performed, for example, based on the intelligent information provided from the previous analysis such as a newly created data object, file and/or process that is now identified as a specimen for subsequent analysis, as described above. Controller 106 may monitor or receive a feedback from any of the static analysis module, dynamic analysis module, emulator, and/or the classifier. Based on a result of any of these components, the controller 106 may modify the analysis plan to include a further analysis of a newly created data object, file and/or process, or alternatively, it may terminate the current analysis if it is determined the result is conclusive.
The static analysis module 860 receives the copy of the network data from the network tap 840 and applies heuristics to the data to determine if the network data might contain suspicious network content. The heuristics applied by the static analysis module 860 may be based on data and/or rules stored in the heuristics database (not shown). The static analysis module 860 may examine the image of the captured content without executing or opening the captured content. For example, the static analysis module 860 may examine the metadata or attributes of the captured content and/or the code image (e.g., a binary image of an executable) to determine whether a certain portion of the captured content matches a predetermined pattern or signature that is associated with a particular type of malicious content. In one example, the static analysis module 860 flags network data as suspicious after applying a heuristic analysis. This detection process is also referred to as static malicious content detection. The suspicious network data may then be provided to the scheduler 870. In some embodiments, the suspicious network data is provided directly to the scheduler 870 with or without buffering or organizing one or more data flows.
When a characteristic of the packet, such as a sequence of characters or keyword, is identified that meets the conditions of a heuristic, a suspicious characteristic of the network content is identified. The identified characteristic may be stored for reference and analysis. In some embodiments, the entire packet may be inspected (e.g., using deep packet inspection techniques) and multiple characteristics may be identified before proceeding to the next step. In some embodiments, the characteristic may be determined as a result of an analysis across multiple packets comprising the network content. A score related to a probability that the suspicious characteristic identified indicates malicious network content is determined.
The static analysis module 860 may also provide a priority level for the packet and/or the features present in the packet. The scheduler 870 may then load and configure a virtual machine from the virtual machine pool 880 in an order related to the priority level, and dispatch the virtual machine to the dynamic analysis module 882 to process the suspicious network content.
The static analysis module 860 may provide the packet containing the suspicious network content to the scheduler 870, along with a list of the features present in the packet and the malicious probability scores associated with each of those features. Alternatively, the static analysis module 860 may provide a pointer to the packet containing the suspicious network content to the scheduler 870 such that the scheduler 870 may access the packet via a memory shared with the static analysis module 860. In another embodiment, the static analysis module 860 may provide identification information regarding the packet to the scheduler 870 such that the scheduler 870, or virtual machine may query the static analysis module 860 for data regarding the packet as needed.
The scheduler 870 may store the received packets, for example, in a queue, and determines an order of processing of the suspicious network content, based on associated priorities assigned to each. The priorities may be based, at least in part, on the results of prior analysis. The scheduler 870 also determines the length of time for processing the suspicious network content based, at least in part, on the results of prior analysis and the waiting queue of network content.
The scheduler 870 may identify an operating environment to be used to process the suspicious network content in a virtual machine, for example, based, at least in part, on the results of the static analysis or other prior analysis. A virtual machine may itself be executable software that is configured with the identified operating environment. The virtual machine may be retrieved from the virtual machine pool 880. Furthermore, the scheduler 870 may identify, for example, an application program required to process the packets, for example, a Web browser, and retrieve a virtual machine equipped with the web browser.
The scheduler 870 may retrieve and configure the virtual machine with features that may include ports that are to receive the network data, select device drivers that are to respond to the network data, and other devices that can respond to the network data. In some embodiments, prior analyses, such as the static analysis, may identified these features. These features may be provided virtually within the virtual environment.
The virtual machine pool 880 may be configured to store one or more virtual machines. The virtual machine pool 880 may include software and/or a storage medium capable of storing software. The virtual machine pool 880 may store any number of distinct virtual machines.
The dynamic analysis module 882 simulates the receipt and/or processing of the network content to analyze the effects (e.g., behaviors) of the network content. There may be multiple dynamic analysis modules 882 to simulate multiple streams of network content. The dynamic analysis module 882 may be configured to monitor the virtual machine for indications that the suspicious network content is in fact malicious network content. Such indications may include unusual network transmissions, unusual changes in performance, and the like. This detection process is referred to as a dynamic malicious content detection.
The dynamic analysis module 882 may flag the suspicious network content as malicious network content according to the observed behavior during processing of the content within the virtual machine. The reporting module 884 may issue alerts indicating the presence of malware, and using pointers and other reference information, identify the packets of the network content containing the malware. This information may include all or an appropriate portion of that stored for the network content in the intelligence store 110. Additionally, the server device 810 may be added to a list of malicious network content providers, and future network transmissions originating from the server device 810 may be blocked from reaching their intended destinations, e.g., by firewall 825.
The computer network system 800 may also include a further communication network 890, which couples the malicious content detection system (MCDS) 850 with one or more other MCDS, of which MCDS 892 and MCDS 894 are shown, and a management system 896, which may be implemented as a Web server having a Web interface. The communication network 890 may, in some embodiments, be coupled for communication with or part of network 820. The management system 896 is responsible for managing the MCDS 850, 892, 894 and providing updates to their operation systems and software programs. Also, the management system 896 may cause malware signatures generated by any of the MCDS 850, 892, 894 to be shared with one or more of the other MCDS 850, 892, 894, for example, on a subscription basis. Moreover, the malicious content detection system as described in the foregoing embodiments may be incorporated into one or more of the MCDS 850, 892, 894, or into all of them, depending on the deployment. Also, the management system 896 itself or another dedicated computer station may incorporate the malicious content detection system in deployments where such detection is to be conducted at a centralized resource.
Further information regarding an embodiment of a malicious content detection system can be had with reference to U.S. Pat. No. 8,171,553, the disclosure of which being incorporated herein by reference in its entirety.
As described above, the detection or analysis performed by the heuristic module 860 may be referred to as static detection or static analysis, which may generate a first score (e.g., a static detection score) according to a first scoring scheme or algorithm. The detection or analysis performed by the analysis engine 882 is referred to as dynamic detection or dynamic analysis, which may generate a second score (e.g., a dynamic detection score) according to a second scoring scheme or algorithm. The first and second scores may be combined, according to a predetermined algorithm, to derive a final score indicating the probability that a malicious content suspect is indeed malicious. Where other analyses are performed, they may result in additional scores may be combined to derive the final score.
Furthermore, detection systems 850 and 892-894 may be deployed in a variety of distribution ways. For example, detection system 850 may be deployed as a detection appliance at a client site to detect any specimen, for example, at a local area network (LAN) of the client. In addition, any of MCDS 892 and MCDS 894 may also be deployed as dedicated data analysis systems. Systems 850 and 892-894 may be configured and managed by a management system 896 over network 890, which may be a LAN, a wide area network (WAN) such as the Internet, or a combination of both. Management system 896 may be implemented as a Web server having a Web interface to allow an administrator of a client (e.g., corporation entity) to log in to manage detection systems 850 and 892-894. For example, an administrator may able to activate or deactivate certain functionalities of malicious content detection systems 850 and 892-894 or alternatively, to distribute software updates such as malicious content definition files (e.g., malicious signatures or patterns) or rules, etc. Furthermore, a user can submit via a Web interface specimen to be analyzed, for example, by dedicated data analysis systems 892-894. As described above, malicious content detection includes static detection and dynamic detection. Such static and dynamic detections can be distributed amongst different systems over a network. For example, static detection may be performed by detection system 850 at a client site, while dynamic detection of the same content can be offloaded to the cloud, for example, by any of detection systems 892-894. Other configurations may exist.
Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the ways used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities.
It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as those set forth in the claims below, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
The techniques shown in the figures can be implemented using code and data stored and executed on one or more electronic devices. Such electronic devices store and communicate (internally and/or with other electronic devices over a network) code and data using computer-readable media, such as non-transitory computer-readable storage media (e.g., magnetic disks; optical disks; random access memory; read only memory; flash memory devices; phase-change memory) and transitory computer-readable transmission media (e.g., electrical, optical, acoustical or other form of propagated signals—such as carrier waves, infrared signals, digital signals).
The processes or methods depicted in the preceding figures may be performed by processing logic that comprises hardware (e.g. circuitry, dedicated logic, etc.), firmware, software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be appreciated that some of the operations described may be performed in a different order. Moreover, some operations may be performed in parallel rather than sequentially.
In the foregoing specification, embodiments of the invention have been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications may be made thereto without departing from the broader spirit and scope of the invention as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense.
This document is a continuation-in-part application, claiming the benefit of, and priority through, U.S. patent application Ser. No. 14/042,420 filed on Sep. 30, 2013, the entirety of which is hereby incorporated by reference.
| 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 |
| 5802277 | Cowlard | Sep 1998 | A |
| 5842002 | Schnurer et al. | Nov 1998 | A |
| 5960170 | Chen et al. | Sep 1999 | A |
| 5978917 | Chi | Nov 1999 | A |
| 5983348 | Ji | Nov 1999 | A |
| 6088803 | Tso et al. | Jul 2000 | A |
| 6092194 | Touboul | Jul 2000 | A |
| 6094677 | Capek et al. | Jul 2000 | A |
| 6108799 | Boulay et al. | Aug 2000 | A |
| 6154844 | Touboul et al. | Nov 2000 | A |
| 6269330 | Cidon et al. | Jul 2001 | B1 |
| 6272641 | Ji | Aug 2001 | B1 |
| 6279113 | Vaidya | Aug 2001 | B1 |
| 6298445 | Shostack et al. | Oct 2001 | B1 |
| 6357008 | Nachenberg | Mar 2002 | B1 |
| 6424627 | Sørhaug et al. | Jul 2002 | B1 |
| 6442696 | Wray et al. | Aug 2002 | B1 |
| 6484315 | Ziese | Nov 2002 | B1 |
| 6487666 | Shanklin et al. | Nov 2002 | B1 |
| 6493756 | O'Brien et al. | Dec 2002 | B1 |
| 6550012 | Villa et al. | Apr 2003 | B1 |
| 6775657 | Baker | Aug 2004 | B1 |
| 6831893 | Ben Nun et al. | Dec 2004 | B1 |
| 6832367 | Choi et al. | Dec 2004 | B1 |
| 6895550 | Kanchirayappa et al. | May 2005 | B2 |
| 6898632 | Gordy et al. | May 2005 | B2 |
| 6907396 | Muttik et al. | Jun 2005 | B1 |
| 6941348 | Petry et al. | Sep 2005 | B2 |
| 6971097 | Wallman | Nov 2005 | B1 |
| 6981279 | Arnold et al. | Dec 2005 | B1 |
| 7007107 | Ivchenko et al. | Feb 2006 | B1 |
| 7028179 | Anderson et al. | Apr 2006 | B2 |
| 7043757 | Hoefelmeyer et al. | May 2006 | B2 |
| 7058822 | Edery et al. | Jun 2006 | B2 |
| 7069316 | Gryaznov | Jun 2006 | B1 |
| 7080407 | Zhao et al. | Jul 2006 | B1 |
| 7080408 | Pak et al. | Jul 2006 | B1 |
| 7093002 | Wolff et al. | Aug 2006 | B2 |
| 7093239 | van der Made | Aug 2006 | B1 |
| 7096498 | Judge | Aug 2006 | B2 |
| 7100201 | Izatt | Aug 2006 | B2 |
| 7107617 | Hursey et al. | Sep 2006 | B2 |
| 7159149 | Spiegel et al. | Jan 2007 | B2 |
| 7213260 | Judge | May 2007 | B2 |
| 7231667 | Jordan | Jun 2007 | B2 |
| 7240364 | Branscomb et al. | Jul 2007 | B1 |
| 7240368 | Roesch et al. | Jul 2007 | B1 |
| 7243371 | Kasper et al. | Jul 2007 | B1 |
| 7249175 | Donaldson | Jul 2007 | B1 |
| 7287278 | Liang | Oct 2007 | B2 |
| 7308716 | Danford et al. | Dec 2007 | B2 |
| 7328453 | Merkle, Jr. et al. | Feb 2008 | B2 |
| 7346486 | Ivancic et al. | Mar 2008 | B2 |
| 7356736 | Natvig | Apr 2008 | B2 |
| 7386888 | Liang et al. | Jun 2008 | B2 |
| 7392542 | Bucher | Jun 2008 | B2 |
| 7418729 | Szor | Aug 2008 | B2 |
| 7428300 | Drew et al. | Sep 2008 | B1 |
| 7441272 | Durham et al. | Oct 2008 | B2 |
| 7448084 | Apap et al. | Nov 2008 | B1 |
| 7458098 | Judge et al. | Nov 2008 | B2 |
| 7464404 | Carpenter et al. | Dec 2008 | B2 |
| 7464407 | Nakae et al. | Dec 2008 | B2 |
| 7467408 | O'Toole, Jr. | Dec 2008 | B1 |
| 7478428 | Thomlinson | Jan 2009 | B1 |
| 7480773 | Reed | Jan 2009 | B1 |
| 7487543 | Arnold et al. | Feb 2009 | B2 |
| 7496960 | Chen et al. | Feb 2009 | B1 |
| 7496961 | Zimmer et al. | Feb 2009 | B2 |
| 7519990 | Xie | Apr 2009 | B1 |
| 7523493 | Liang et al. | Apr 2009 | B2 |
| 7530104 | Thrower et al. | May 2009 | B1 |
| 7540025 | Tzadikario | May 2009 | B2 |
| 7540030 | Zaitsev | May 2009 | B1 |
| 7546638 | Anderson et al. | Jun 2009 | B2 |
| 7565550 | Liang et al. | Jul 2009 | B2 |
| 7568233 | Szor et al. | Jul 2009 | B1 |
| 7584455 | Ball | Sep 2009 | B2 |
| 7603715 | Costa et al. | Oct 2009 | B2 |
| 7607171 | Marsden et al. | Oct 2009 | B1 |
| 7639714 | Stolfo et al. | Dec 2009 | B2 |
| 7644441 | Schmid et al. | Jan 2010 | B2 |
| 7657419 | van der Made | Feb 2010 | B2 |
| 7676841 | Sobchuk et al. | Mar 2010 | B2 |
| 7698548 | Shelest et al. | Apr 2010 | B2 |
| 7707633 | Danford et al. | Apr 2010 | B2 |
| 7712136 | Sprosts et al. | May 2010 | B2 |
| 7730011 | Deninger et al. | Jun 2010 | B1 |
| 7739740 | Nachenberg et al. | Jun 2010 | B1 |
| 7779463 | Stolfo et al. | Aug 2010 | B2 |
| 7784097 | Stolfo et al. | Aug 2010 | B1 |
| 7832008 | Kraemer | Nov 2010 | B1 |
| 7836502 | Zhao et al. | Nov 2010 | B1 |
| 7849506 | Dansey et al. | Dec 2010 | B1 |
| 7854007 | Sprosts et al. | Dec 2010 | B2 |
| 7869073 | Oshima | Jan 2011 | B2 |
| 7877803 | Enstone et al. | Jan 2011 | B2 |
| 7904959 | Sidiroglou et al. | Mar 2011 | B2 |
| 7908653 | Brickell | Mar 2011 | B2 |
| 7908660 | Bahl | Mar 2011 | B2 |
| 7930738 | Petersen | Apr 2011 | B1 |
| 7937387 | Frazier et al. | May 2011 | B2 |
| 7937761 | Bennett | May 2011 | B1 |
| 7949849 | Lowe et al. | May 2011 | B2 |
| 7996556 | Raghavan et al. | Aug 2011 | B2 |
| 7996836 | McCorkendale et al. | Aug 2011 | B1 |
| 7996904 | Chiueh et al. | Aug 2011 | B1 |
| 7996905 | Arnold et al. | Aug 2011 | B2 |
| 8006305 | Aziz | Aug 2011 | B2 |
| 8010667 | Zhang et al. | Aug 2011 | B2 |
| 8020206 | Hubbard et al. | Sep 2011 | B2 |
| 8028338 | Schneider et al. | Sep 2011 | B1 |
| 8042184 | Batenin | Oct 2011 | B1 |
| 8045094 | Teragawa | Oct 2011 | B2 |
| 8045458 | Alperovitch et al. | Oct 2011 | B2 |
| 8069484 | McMillan et al. | Nov 2011 | B2 |
| 8087086 | Lai et al. | Dec 2011 | B1 |
| 8171553 | Aziz et al. | May 2012 | B2 |
| 8176049 | Deninger et al. | May 2012 | B2 |
| 8176480 | Spertus | May 2012 | B1 |
| 8201246 | Wu et al. | Jun 2012 | B1 |
| 8204984 | Aziz et al. | Jun 2012 | B1 |
| 8214905 | Doukhvalov et al. | Jul 2012 | B1 |
| 8220055 | Kennedy | Jul 2012 | B1 |
| 8225288 | Miller et al. | Jul 2012 | B2 |
| 8225373 | Kraemer | Jul 2012 | B2 |
| 8233882 | Rogel | Jul 2012 | B2 |
| 8234640 | Fitzgerald et al. | Jul 2012 | B1 |
| 8234709 | Viljoen et al. | Jul 2012 | B2 |
| 8239944 | Nachenberg et al. | Aug 2012 | B1 |
| 8260914 | Ranjan | Sep 2012 | B1 |
| 8266091 | Gubin et al. | Sep 2012 | B1 |
| 8286251 | Eker et al. | Oct 2012 | B2 |
| 8291499 | Aziz et al. | Oct 2012 | B2 |
| 8307435 | Mann et al. | Nov 2012 | B1 |
| 8307443 | Wang et al. | Nov 2012 | B2 |
| 8312545 | Tuvell et al. | Nov 2012 | B2 |
| 8321936 | Green et al. | Nov 2012 | B1 |
| 8321941 | Tuvell et al. | Nov 2012 | B2 |
| 8332571 | Edwards, Sr. | Dec 2012 | B1 |
| 8365286 | Poston | Jan 2013 | B2 |
| 8365297 | Parshin et al. | Jan 2013 | B1 |
| 8370938 | Daswani et al. | Feb 2013 | B1 |
| 8370939 | Zaitsev et al. | Feb 2013 | B2 |
| 8375444 | Aziz et al. | Feb 2013 | B2 |
| 8381299 | Stolfo et al. | Feb 2013 | B2 |
| 8402529 | Green et al. | Mar 2013 | B1 |
| 8464340 | Ahn et al. | Jun 2013 | B2 |
| 8479174 | Chiriac | Jul 2013 | B2 |
| 8479276 | Vaystikh et al. | Jul 2013 | B1 |
| 8479291 | Bodke | Jul 2013 | B1 |
| 8510827 | Leake et al. | Aug 2013 | B1 |
| 8510828 | Guo et al. | Aug 2013 | B1 |
| 8510842 | Amit et al. | Aug 2013 | B2 |
| 8516478 | Edwards et al. | Aug 2013 | B1 |
| 8516590 | Ranadive et al. | Aug 2013 | B1 |
| 8516593 | Aziz | Aug 2013 | B2 |
| 8522348 | Chen et al. | Aug 2013 | B2 |
| 8528086 | Aziz | Sep 2013 | B1 |
| 8533824 | Hutton et al. | Sep 2013 | B2 |
| 8539582 | Aziz et al. | Sep 2013 | B1 |
| 8549638 | Aziz | Oct 2013 | B2 |
| 8555391 | Demir et al. | Oct 2013 | B1 |
| 8561177 | Aziz et al. | Oct 2013 | B1 |
| 8566476 | Shiffer et al. | Oct 2013 | B2 |
| 8566946 | Aziz et al. | Oct 2013 | B1 |
| 8584094 | Dadhia et al. | Nov 2013 | B2 |
| 8584234 | Sobel et al. | Nov 2013 | B1 |
| 8584239 | Aziz et al. | Nov 2013 | B2 |
| 8595834 | Xie et al. | Nov 2013 | B2 |
| 8627476 | Satish et al. | Jan 2014 | B1 |
| 8635696 | Aziz | Jan 2014 | B1 |
| 8682054 | Xue et al. | Mar 2014 | B2 |
| 8682812 | Ranjan | Mar 2014 | B1 |
| 8689333 | Aziz | Apr 2014 | B2 |
| 8695096 | Zhang | Apr 2014 | B1 |
| 8713631 | Pavlyushchik | Apr 2014 | B1 |
| 8713681 | Silberman et al. | Apr 2014 | B2 |
| 8726392 | McCorkendale et al. | May 2014 | B1 |
| 8739280 | Chess et al. | May 2014 | B2 |
| 8776229 | Aziz | Jul 2014 | B1 |
| 8782792 | Bodke | Jul 2014 | B1 |
| 8789172 | Stolfo et al. | Jul 2014 | B2 |
| 8789178 | Kejriwal et al. | Jul 2014 | B2 |
| 8793278 | Frazier et al. | Jul 2014 | B2 |
| 8793787 | Ismael et al. | Jul 2014 | B2 |
| 8805947 | Kuzkin et al. | Aug 2014 | B1 |
| 8806647 | Daswani et al. | Aug 2014 | B1 |
| 8832829 | Manni et al. | Sep 2014 | B2 |
| 8850570 | Ramzan | Sep 2014 | B1 |
| 8850571 | Staniford et al. | Sep 2014 | B2 |
| 8881234 | Narasimhan et al. | Nov 2014 | B2 |
| 8881271 | Butler, II | Nov 2014 | B2 |
| 8881282 | Aziz et al. | Nov 2014 | B1 |
| 8898788 | Aziz et al. | Nov 2014 | B1 |
| 8935779 | Manni et al. | Jan 2015 | B2 |
| 8949257 | Shiffer et al. | Feb 2015 | B2 |
| 8984638 | Aziz et al. | Mar 2015 | B1 |
| 8990939 | Staniford et al. | Mar 2015 | B2 |
| 8990944 | Singh et al. | Mar 2015 | B1 |
| 8997219 | Staniford et al. | Mar 2015 | B2 |
| 9009822 | Ismael et al. | Apr 2015 | B1 |
| 9009823 | Ismael et al. | Apr 2015 | B1 |
| 9027135 | Aziz | May 2015 | B1 |
| 9071638 | Aziz et al. | Jun 2015 | B1 |
| 9104867 | Thioux et al. | Aug 2015 | B1 |
| 9106630 | Frazier et al. | Aug 2015 | B2 |
| 9106694 | Aziz et al. | Aug 2015 | B2 |
| 9118715 | Staniford et al. | Aug 2015 | B2 |
| 9159035 | Ismael et al. | Oct 2015 | B1 |
| 9171160 | Vincent et al. | Oct 2015 | B2 |
| 9176843 | Ismael et al. | Nov 2015 | B1 |
| 9189627 | Islam | Nov 2015 | B1 |
| 9195829 | Goradia et al. | Nov 2015 | B1 |
| 9197664 | Aziz et al. | Nov 2015 | B1 |
| 9223972 | Vincent et al. | Dec 2015 | B1 |
| 9225740 | Ismael et al. | Dec 2015 | B1 |
| 9241010 | Bennett et al. | Jan 2016 | B1 |
| 9251343 | Vincent | Feb 2016 | B1 |
| 9262635 | Paithane et al. | Feb 2016 | B2 |
| 9268936 | Butler | Feb 2016 | B2 |
| 9275229 | LeMasters | Mar 2016 | B2 |
| 9282109 | Aziz et al. | Mar 2016 | B1 |
| 9292686 | Ismael et al. | Mar 2016 | B2 |
| 9294501 | Mesdaq et al. | Mar 2016 | B2 |
| 9300686 | Pidathala et al. | Mar 2016 | B2 |
| 9306960 | Aziz | Apr 2016 | B1 |
| 9306974 | Aziz et al. | Apr 2016 | B1 |
| 9311479 | Manni et al. | Apr 2016 | B1 |
| 9355247 | Thioux et al. | May 2016 | B1 |
| 9356944 | Aziz | May 2016 | B1 |
| 9363280 | Rivlin et al. | Jun 2016 | B1 |
| 9367681 | Ismael et al. | Jun 2016 | B1 |
| 9398028 | Karandikar et al. | Jul 2016 | B1 |
| 9413781 | Cunningham et al. | Aug 2016 | B2 |
| 9426071 | Caldejon et al. | Aug 2016 | B1 |
| 9430646 | Mushtaq et al. | Aug 2016 | B1 |
| 9432389 | Khalid et al. | Aug 2016 | B1 |
| 9438613 | Paithane et al. | Sep 2016 | B1 |
| 9438622 | Staniford et al. | Sep 2016 | B1 |
| 9438623 | Thioux et al. | Sep 2016 | B1 |
| 9459901 | Jung et al. | Oct 2016 | B2 |
| 9467460 | Otvagin et al. | Oct 2016 | B1 |
| 9483644 | Paithane et al. | Nov 2016 | B1 |
| 9495180 | Ismael | Nov 2016 | B2 |
| 9497213 | Thompson et al. | Nov 2016 | B2 |
| 9507935 | Ismael et al. | Nov 2016 | B2 |
| 9516057 | Aziz | Dec 2016 | B2 |
| 9519782 | Aziz et al. | Dec 2016 | B2 |
| 9536091 | Paithane et al. | Jan 2017 | B2 |
| 9537972 | Edwards et al. | Jan 2017 | B1 |
| 9560059 | Islam | Jan 2017 | B1 |
| 9565202 | Kindlund et al. | Feb 2017 | B1 |
| 9591015 | Amin et al. | Mar 2017 | B1 |
| 9591020 | Aziz | Mar 2017 | B1 |
| 9594904 | Jain et al. | Mar 2017 | B1 |
| 9594905 | Ismael et al. | Mar 2017 | B1 |
| 9594912 | Thioux et al. | Mar 2017 | B1 |
| 9609007 | Rivlin et al. | Mar 2017 | B1 |
| 9626509 | Khalid et al. | Apr 2017 | B1 |
| 9628498 | Aziz et al. | Apr 2017 | B1 |
| 9628507 | Haq et al. | Apr 2017 | B2 |
| 9633134 | Ross | Apr 2017 | B2 |
| 9635039 | Islam et al. | Apr 2017 | B1 |
| 9641546 | Manni et al. | May 2017 | B1 |
| 9654485 | Neumann | May 2017 | B1 |
| 9661009 | Karandikar et al. | May 2017 | B1 |
| 9661018 | Aziz | May 2017 | B1 |
| 9674298 | Edwards et al. | Jun 2017 | B1 |
| 9680862 | Ismael et al. | Jun 2017 | B2 |
| 9690606 | Ha et al. | Jun 2017 | B1 |
| 9690933 | Singh et al. | Jun 2017 | B1 |
| 9690935 | Shifter et al. | Jun 2017 | B2 |
| 9690936 | Malik et al. | Jun 2017 | B1 |
| 9736179 | Ismael | Aug 2017 | B2 |
| 9740857 | Ismael et al. | Aug 2017 | B2 |
| 9747446 | Pidathala et al. | Aug 2017 | B1 |
| 9756074 | Aziz et al. | Sep 2017 | B2 |
| 9773112 | Rathor et al. | Sep 2017 | B1 |
| 9781144 | Otvagin et al. | Oct 2017 | B1 |
| 9787700 | Amin | Oct 2017 | B1 |
| 9787706 | Otvagin et al. | Oct 2017 | B1 |
| 9792196 | Ismael et al. | Oct 2017 | B1 |
| 9824209 | Ismael et al. | Nov 2017 | B1 |
| 9824211 | Wilson | Nov 2017 | B2 |
| 9824216 | Khalid et al. | Nov 2017 | B1 |
| 9825976 | Gomez et al. | Nov 2017 | B1 |
| 9825989 | Mehra et al. | Nov 2017 | B1 |
| 9838408 | Karandikar et al. | Dec 2017 | B1 |
| 9838411 | Aziz | Dec 2017 | B1 |
| 9838416 | Aziz | Dec 2017 | B1 |
| 9838417 | Khalid et al. | Dec 2017 | B1 |
| 9846776 | Paithane et al. | Dec 2017 | B1 |
| 9876701 | Caldejon et al. | Jan 2018 | B1 |
| 9888016 | Amin et al. | Feb 2018 | B1 |
| 9888019 | Pidathala et al. | Feb 2018 | B1 |
| 9910988 | Vincent | Mar 2018 | B1 |
| 9912644 | Cunningham | Mar 2018 | B2 |
| 9912681 | Ismael et al. | Mar 2018 | B1 |
| 9912684 | Aziz et al. | Mar 2018 | B1 |
| 9912691 | Mesdaq et al. | Mar 2018 | B2 |
| 9912698 | Thioux et al. | Mar 2018 | B1 |
| 9916440 | Paithane et al. | Mar 2018 | B1 |
| 9921978 | Chan et al. | Mar 2018 | B1 |
| 9934376 | Ismael | Apr 2018 | B1 |
| 9934381 | Kindlund et al. | Apr 2018 | B1 |
| 9946568 | Ismael et al. | Apr 2018 | B1 |
| 9954890 | Staniford et al. | Apr 2018 | B1 |
| 9973531 | Thioux | May 2018 | B1 |
| 10002252 | Ismael et al. | Jun 2018 | B2 |
| 10019338 | Goradia et al. | Jul 2018 | B1 |
| 10019573 | Silberman et al. | Jul 2018 | B2 |
| 10025691 | Ismael et al. | Jul 2018 | B1 |
| 10025927 | Khalid et al. | Jul 2018 | B1 |
| 10027689 | Rathor et al. | Jul 2018 | B1 |
| 10027690 | Aziz et al. | Jul 2018 | B2 |
| 10027696 | Rivlin et al. | Jul 2018 | B1 |
| 10033747 | Paithane et al. | Jul 2018 | B1 |
| 10033748 | Cunningham et al. | Jul 2018 | B1 |
| 10033753 | Islam et al. | Jul 2018 | B1 |
| 10033759 | Kabra et al. | Jul 2018 | B1 |
| 10050998 | Singh | Aug 2018 | B1 |
| 10068091 | Aziz et al. | Sep 2018 | B1 |
| 10075455 | Zafar et al. | Sep 2018 | B2 |
| 10083302 | Paithane et al. | Sep 2018 | B1 |
| 10084813 | Eyada | Sep 2018 | B2 |
| 10089461 | Ha et al. | Oct 2018 | B1 |
| 10097573 | Aziz | Oct 2018 | B1 |
| 10104102 | Neumann | Oct 2018 | B1 |
| 10108446 | Steinberg et al. | Oct 2018 | B1 |
| 10121000 | Rivlin et al. | Nov 2018 | B1 |
| 10122746 | Manni et al. | Nov 2018 | B1 |
| 10133863 | Bu et al. | Nov 2018 | B2 |
| 10133866 | Kumar et al. | Nov 2018 | B1 |
| 10146810 | Shiffer et al. | Dec 2018 | B2 |
| 10148693 | Singh et al. | Dec 2018 | B2 |
| 10165000 | Aziz et al. | Dec 2018 | B1 |
| 10169585 | Pilipenko et al. | Jan 2019 | B1 |
| 10176321 | Abbasi et al. | Jan 2019 | B2 |
| 10181029 | Ismael et al. | Jan 2019 | B1 |
| 10191861 | Steinberg et al. | Jan 2019 | B1 |
| 10192052 | Singh et al. | Jan 2019 | B1 |
| 10198574 | Thioux et al. | Feb 2019 | B1 |
| 10200384 | Mushtaq et al. | Feb 2019 | B1 |
| 10210329 | Malik et al. | Feb 2019 | B1 |
| 10216927 | Steinberg | Feb 2019 | B1 |
| 10218740 | Mesdaq et al. | Feb 2019 | B1 |
| 10242185 | Goradia | Mar 2019 | B1 |
| 20010005889 | Albrecht | Jun 2001 | A1 |
| 20010047326 | Broadbent et al. | Nov 2001 | A1 |
| 20020018903 | Kokubo et al. | Feb 2002 | A1 |
| 20020038430 | Edwards et al. | Mar 2002 | A1 |
| 20020091819 | Melchione et al. | Jul 2002 | A1 |
| 20020095607 | Lin-Hendel | Jul 2002 | A1 |
| 20020116627 | Tarbotton et al. | Aug 2002 | A1 |
| 20020144156 | Copeland | Oct 2002 | A1 |
| 20020162015 | Tang | Oct 2002 | A1 |
| 20020166063 | Lachman et al. | Nov 2002 | A1 |
| 20020169952 | DiSanto et al. | Nov 2002 | A1 |
| 20020184528 | Shevenell et al. | Dec 2002 | A1 |
| 20020188887 | Largman et al. | Dec 2002 | A1 |
| 20020194490 | Halperin et al. | Dec 2002 | A1 |
| 20030021728 | Shame et al. | Jan 2003 | A1 |
| 20030074578 | Ford et al. | Apr 2003 | A1 |
| 20030084318 | Schertz | May 2003 | A1 |
| 20030101381 | Mateev et al. | May 2003 | A1 |
| 20030115483 | Liang | Jun 2003 | A1 |
| 20030188190 | Aaron et al. | Oct 2003 | A1 |
| 20030191957 | Hypponen et al. | Oct 2003 | A1 |
| 20030200460 | Morota et al. | Oct 2003 | A1 |
| 20030212902 | van der Made | Nov 2003 | A1 |
| 20030229801 | Kouznetsov et al. | Dec 2003 | A1 |
| 20030237000 | Denton et al. | Dec 2003 | A1 |
| 20040003323 | Bennett et al. | Jan 2004 | A1 |
| 20040006473 | Mills et al. | Jan 2004 | A1 |
| 20040015712 | Szor | Jan 2004 | A1 |
| 20040019832 | Arnold et al. | Jan 2004 | A1 |
| 20040047356 | Bauer | Mar 2004 | A1 |
| 20040083408 | Spiegel et al. | Apr 2004 | A1 |
| 20040088581 | Brawn et al. | May 2004 | A1 |
| 20040093513 | Cantrell et al. | May 2004 | A1 |
| 20040111531 | Staniford et al. | Jun 2004 | A1 |
| 20040117478 | Triulzi et al. | Jun 2004 | A1 |
| 20040117624 | Brandt et al. | Jun 2004 | A1 |
| 20040128355 | Chao et al. | Jul 2004 | A1 |
| 20040165588 | Pandya | Aug 2004 | A1 |
| 20040236963 | Danford et al. | Nov 2004 | A1 |
| 20040243349 | Greifeneder et al. | Dec 2004 | A1 |
| 20040249911 | Alkhatib et al. | Dec 2004 | A1 |
| 20040255161 | Cavanaugh | Dec 2004 | A1 |
| 20040268147 | Wiederin et al. | Dec 2004 | A1 |
| 20050005159 | Oliphant | Jan 2005 | A1 |
| 20050021740 | Bar et al. | Jan 2005 | A1 |
| 20050033960 | Vialen et al. | Feb 2005 | A1 |
| 20050033989 | Poletto et al. | Feb 2005 | A1 |
| 20050050148 | Mohammadioun et al. | Mar 2005 | A1 |
| 20050086523 | Zimmer et al. | Apr 2005 | A1 |
| 20050091513 | Mitomo et al. | Apr 2005 | A1 |
| 20050091533 | Omote et al. | Apr 2005 | A1 |
| 20050091652 | Ross et al. | Apr 2005 | A1 |
| 20050108562 | Khazan et al. | May 2005 | A1 |
| 20050114663 | Cornell et al. | May 2005 | A1 |
| 20050125195 | Brendel | Jun 2005 | A1 |
| 20050149726 | Joshi et al. | Jul 2005 | A1 |
| 20050157662 | Bingham et al. | Jul 2005 | A1 |
| 20050183143 | Anderholm et al. | Aug 2005 | A1 |
| 20050201297 | Peikari | Sep 2005 | A1 |
| 20050210533 | Copeland et al. | Sep 2005 | A1 |
| 20050238005 | Chen et al. | Oct 2005 | A1 |
| 20050240781 | Gassoway | Oct 2005 | A1 |
| 20050262562 | Gassoway | Nov 2005 | A1 |
| 20050265331 | Stolfo | Dec 2005 | A1 |
| 20050283839 | Cowbum | Dec 2005 | A1 |
| 20060010495 | Cohen et al. | Jan 2006 | A1 |
| 20060015416 | Hoffman et al. | Jan 2006 | A1 |
| 20060015715 | Anderson | Jan 2006 | A1 |
| 20060015747 | Van de Ven | Jan 2006 | A1 |
| 20060021029 | Brickell et al. | Jan 2006 | A1 |
| 20060021054 | Costa et al. | Jan 2006 | A1 |
| 20060031476 | Mathes et al. | Feb 2006 | A1 |
| 20060037079 | Midgley | Feb 2006 | A1 |
| 20060047665 | Neil | Mar 2006 | A1 |
| 20060070130 | Costea et al. | Mar 2006 | A1 |
| 20060075496 | Carpenter et al. | Apr 2006 | A1 |
| 20060095968 | Portolani et al. | May 2006 | A1 |
| 20060101516 | Sudaharan et al. | May 2006 | A1 |
| 20060101517 | Banzhof et al. | May 2006 | A1 |
| 20060117385 | Mester et al. | Jun 2006 | A1 |
| 20060123477 | Raghavan et al. | Jun 2006 | A1 |
| 20060143709 | Brooks et al. | Jun 2006 | A1 |
| 20060150249 | Gassen et al. | Jul 2006 | A1 |
| 20060161983 | Cothrell et al. | Jul 2006 | A1 |
| 20060161987 | Levy-Yurista | Jul 2006 | A1 |
| 20060161989 | Reshef et al. | Jul 2006 | A1 |
| 20060164199 | Gilde et al. | Jul 2006 | A1 |
| 20060173992 | Weber | Aug 2006 | A1 |
| 20060179147 | Tran et al. | Aug 2006 | A1 |
| 20060184632 | Marino et al. | Aug 2006 | A1 |
| 20060191010 | Benjamin | Aug 2006 | A1 |
| 20060221956 | Narayan et al. | Oct 2006 | A1 |
| 20060236393 | Kramer et al. | Oct 2006 | A1 |
| 20060242709 | Seinfeld et al. | Oct 2006 | A1 |
| 20060248519 | Jaeger et al. | Nov 2006 | A1 |
| 20060248582 | Panjwani et al. | Nov 2006 | A1 |
| 20060251104 | Koga | Nov 2006 | A1 |
| 20060288417 | Bookbinder et al. | Dec 2006 | A1 |
| 20070006288 | Mayfield et al. | Jan 2007 | A1 |
| 20070006313 | Porras et al. | Jan 2007 | A1 |
| 20070011174 | Takaragi et al. | Jan 2007 | A1 |
| 20070016951 | Piccard et al. | Jan 2007 | A1 |
| 20070019286 | Kikuchi | Jan 2007 | A1 |
| 20070033645 | Jones | Feb 2007 | A1 |
| 20070038943 | FitzGerald et al. | Feb 2007 | A1 |
| 20070064689 | Shin et al. | Mar 2007 | A1 |
| 20070074169 | Chess | 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 |
| 20070192863 | Kapoor et al. | Aug 2007 | A1 |
| 20070198275 | Malden et al. | Aug 2007 | A1 |
| 20070208822 | Wang et al. | Sep 2007 | A1 |
| 20070220607 | Sprosts et al. | Sep 2007 | A1 |
| 20070240218 | Tuvell et al. | Oct 2007 | A1 |
| 20070240219 | Tuvell et al. | Oct 2007 | A1 |
| 20070240220 | Tuvell et al. | Oct 2007 | A1 |
| 20070240222 | Tuvell et al. | Oct 2007 | A1 |
| 20070250930 | Aziz et al. | Oct 2007 | A1 |
| 20070256132 | Oliphant | Nov 2007 | A2 |
| 20070271446 | Nakamura | Nov 2007 | A1 |
| 20080005782 | Aziz | Jan 2008 | A1 |
| 20080018122 | Zierler et al. | Jan 2008 | A1 |
| 20080028463 | Dagon et al. | Jan 2008 | A1 |
| 20080040710 | Chiriac | Feb 2008 | A1 |
| 20080046781 | Childs et al. | Feb 2008 | A1 |
| 20080066179 | Liu | Mar 2008 | A1 |
| 20080072326 | Danford et al. | Mar 2008 | A1 |
| 20080077793 | Tan et al. | Mar 2008 | A1 |
| 20080080518 | Hoeflin et al. | Apr 2008 | A1 |
| 20080086720 | Lekel | Apr 2008 | A1 |
| 20080098476 | Syversen | Apr 2008 | A1 |
| 20080120722 | Sima et al. | May 2008 | A1 |
| 20080134178 | Fitzgerald et al. | Jun 2008 | A1 |
| 20080134334 | Kim et al. | Jun 2008 | A1 |
| 20080141376 | Clausen et al. | Jun 2008 | A1 |
| 20080184367 | McMillan et al. | Jul 2008 | A1 |
| 20080184373 | Traut et al. | Jul 2008 | A1 |
| 20080189787 | Arnold et al. | Aug 2008 | A1 |
| 20080201778 | Guo et al. | Aug 2008 | A1 |
| 20080209557 | Herley et al. | Aug 2008 | A1 |
| 20080215742 | Goldszmidt et al. | Sep 2008 | A1 |
| 20080222729 | Chen et al. | Sep 2008 | A1 |
| 20080263665 | Ma et al. | Oct 2008 | A1 |
| 20080295172 | Bohacek | Nov 2008 | A1 |
| 20080301810 | Lehane et al. | Dec 2008 | A1 |
| 20080307524 | Singh et al. | Dec 2008 | A1 |
| 20080313738 | Enderby | Dec 2008 | A1 |
| 20080320594 | Jiang | Dec 2008 | A1 |
| 20090003317 | Kasralikar et al. | Jan 2009 | A1 |
| 20090007100 | Field et al. | Jan 2009 | A1 |
| 20090013408 | Schipka | Jan 2009 | A1 |
| 20090031423 | Liu et al. | Jan 2009 | A1 |
| 20090036111 | Danford et al. | Feb 2009 | A1 |
| 20090037835 | Goldman | Feb 2009 | A1 |
| 20090044024 | Oberheide et al. | Feb 2009 | A1 |
| 20090044272 | Jarrett | Feb 2009 | A1 |
| 20090044274 | Budko et al. | Feb 2009 | A1 |
| 20090064332 | Porras et al. | Mar 2009 | A1 |
| 20090077666 | Chen et al. | Mar 2009 | A1 |
| 20090083369 | Marmor | Mar 2009 | A1 |
| 20090083855 | Apap et al. | Mar 2009 | A1 |
| 20090089879 | Wang et al. | Apr 2009 | A1 |
| 20090094697 | Provos et al. | Apr 2009 | A1 |
| 20090113425 | Ports et al. | Apr 2009 | A1 |
| 20090125976 | Wassermann et al. | May 2009 | A1 |
| 20090126015 | Monastyrsky et al. | May 2009 | A1 |
| 20090126016 | Sobko et al. | May 2009 | A1 |
| 20090133125 | Choi et al. | May 2009 | A1 |
| 20090144823 | Lamastra et al. | Jun 2009 | A1 |
| 20090158430 | Borders | Jun 2009 | A1 |
| 20090172815 | Gu et al. | Jul 2009 | A1 |
| 20090187992 | Poston | Jul 2009 | A1 |
| 20090193293 | Stolfo et al. | Jul 2009 | A1 |
| 20090198651 | Shiffer et al. | Aug 2009 | A1 |
| 20090198670 | Shiffer et al. | Aug 2009 | A1 |
| 20090198689 | Frazier et al. | Aug 2009 | A1 |
| 20090199274 | Frazier et al. | Aug 2009 | A1 |
| 20090199296 | Xie et al. | Aug 2009 | A1 |
| 20090228233 | Anderson et al. | Sep 2009 | A1 |
| 20090241187 | Troyansky | Sep 2009 | A1 |
| 20090241190 | Todd et al. | Sep 2009 | A1 |
| 20090265692 | Godefroid et al. | Oct 2009 | A1 |
| 20090271867 | Zhang | Oct 2009 | A1 |
| 20090282485 | Bennett | Nov 2009 | A1 |
| 20090300415 | Zhang et al. | Dec 2009 | A1 |
| 20090300761 | Park et al. | Dec 2009 | A1 |
| 20090328185 | Berg et al. | Dec 2009 | A1 |
| 20090328221 | Blumfield et al. | Dec 2009 | A1 |
| 20100005146 | Drako et al. | Jan 2010 | A1 |
| 20100011205 | McKenna | Jan 2010 | A1 |
| 20100017546 | Poo et al. | Jan 2010 | A1 |
| 20100030996 | Butler, II | Feb 2010 | A1 |
| 20100031353 | Thomas et al. | Feb 2010 | A1 |
| 20100037314 | Perdisci et al. | Feb 2010 | A1 |
| 20100043073 | Kuwamura | Feb 2010 | A1 |
| 20100054278 | Stolfo et al. | Mar 2010 | A1 |
| 20100058474 | Hicks | Mar 2010 | A1 |
| 20100064044 | Nonoyama | Mar 2010 | A1 |
| 20100077481 | Polyakov et al. | Mar 2010 | A1 |
| 20100083376 | Pereira et al. | Apr 2010 | A1 |
| 20100115621 | Staniford et al. | May 2010 | A1 |
| 20100132038 | Zaitsev | May 2010 | A1 |
| 20100154056 | Smith et al. | Jun 2010 | A1 |
| 20100180344 | Malyshev et al. | Jul 2010 | A1 |
| 20100192223 | Ismael et al. | Jul 2010 | A1 |
| 20100220863 | Dupaquis et al. | Sep 2010 | A1 |
| 20100235831 | Dittmer | Sep 2010 | A1 |
| 20100251104 | Massand | Sep 2010 | A1 |
| 20100269095 | King | Oct 2010 | A1 |
| 20100281102 | Chinta et al. | Nov 2010 | A1 |
| 20100281541 | Stolfo et al. | Nov 2010 | A1 |
| 20100281542 | Stolfo et al. | Nov 2010 | A1 |
| 20100287260 | Peterson et al. | Nov 2010 | A1 |
| 20100299754 | Amit et al. | Nov 2010 | A1 |
| 20100306173 | Frank | Dec 2010 | A1 |
| 20110004737 | Greenebaum | Jan 2011 | A1 |
| 20110025504 | Lyon et al. | Feb 2011 | A1 |
| 20110041179 | St Hlberg | Feb 2011 | A1 |
| 20110047594 | Mahaffey et al. | Feb 2011 | A1 |
| 20110047620 | Mahaffey et al. | Feb 2011 | A1 |
| 20110055907 | Narasimhan et al. | Mar 2011 | A1 |
| 20110078794 | Manni et al. | Mar 2011 | A1 |
| 20110093951 | Aziz | Apr 2011 | A1 |
| 20110099620 | Stavrou et al. | Apr 2011 | A1 |
| 20110099633 | Aziz | Apr 2011 | A1 |
| 20110099635 | Silberman et al. | Apr 2011 | A1 |
| 20110113231 | Kaminsky | May 2011 | A1 |
| 20110145918 | Jung et al. | Jun 2011 | A1 |
| 20110145920 | Mahaffey et al. | Jun 2011 | A1 |
| 20110145934 | Abramovici et al. | Jun 2011 | A1 |
| 20110167493 | Song et al. | Jul 2011 | A1 |
| 20110167494 | Bowen et al. | Jul 2011 | A1 |
| 20110173213 | Frazier et al. | Jul 2011 | A1 |
| 20110173460 | Ito et al. | Jul 2011 | A1 |
| 20110219449 | St. Neitzel et al. | Sep 2011 | A1 |
| 20110219450 | McDougal et al. | Sep 2011 | A1 |
| 20110225624 | Sawhney et al. | Sep 2011 | A1 |
| 20110225655 | Niemela et al. | Sep 2011 | A1 |
| 20110247072 | Staniford et al. | Oct 2011 | A1 |
| 20110265182 | Peinado et al. | Oct 2011 | A1 |
| 20110289582 | Kejriwal et al. | Nov 2011 | A1 |
| 20110302587 | Nishikawa et al. | Dec 2011 | A1 |
| 20110307954 | Melnik et al. | Dec 2011 | A1 |
| 20110307955 | Kaplan et al. | Dec 2011 | A1 |
| 20110307956 | Yermakov et al. | Dec 2011 | A1 |
| 20110314546 | Aziz et al. | Dec 2011 | A1 |
| 20120023593 | Puder et al. | Jan 2012 | A1 |
| 20120054869 | Yen et al. | Mar 2012 | A1 |
| 20120066698 | Yanoo | Mar 2012 | A1 |
| 20120079596 | Thomas et al. | Mar 2012 | A1 |
| 20120084859 | Radinsky et al. | Apr 2012 | A1 |
| 20120096553 | Srivastava et al. | Apr 2012 | A1 |
| 20120110667 | Zubrilin et al. | May 2012 | A1 |
| 20120117652 | Manni et al. | May 2012 | A1 |
| 20120121154 | Xue et al. | May 2012 | A1 |
| 20120124426 | Maybee et al. | May 2012 | A1 |
| 20120174186 | Aziz et al. | Jul 2012 | A1 |
| 20120174196 | Bhogavilli et al. | Jul 2012 | A1 |
| 20120174218 | McCoy et al. | Jul 2012 | A1 |
| 20120198279 | Schroeder | Aug 2012 | A1 |
| 20120210423 | Friedrichs et al. | Aug 2012 | A1 |
| 20120222120 | Rim | Aug 2012 | A1 |
| 20120222121 | Staniford et al. | Aug 2012 | A1 |
| 20120255015 | Sahita et al. | Oct 2012 | A1 |
| 20120255017 | Sallam | Oct 2012 | A1 |
| 20120260342 | Dube et al. | Oct 2012 | A1 |
| 20120266244 | Green et al. | Oct 2012 | A1 |
| 20120278886 | Luna | Nov 2012 | A1 |
| 20120297489 | Dequevy | Nov 2012 | A1 |
| 20120311713 | Amit | Dec 2012 | A1 |
| 20120330801 | McDougal et al. | Dec 2012 | A1 |
| 20120331553 | Aziz et al. | Dec 2012 | A1 |
| 20130014259 | Gribble et al. | Jan 2013 | A1 |
| 20130036472 | Aziz | Feb 2013 | A1 |
| 20130047257 | Aziz | Feb 2013 | A1 |
| 20130074185 | McDougal et al. | Mar 2013 | A1 |
| 20130086684 | Mohler | Apr 2013 | A1 |
| 20130091571 | Lu | Apr 2013 | A1 |
| 20130097699 | Balupari et al. | Apr 2013 | A1 |
| 20130097706 | Titonis et al. | Apr 2013 | A1 |
| 20130111587 | Goel et al. | May 2013 | A1 |
| 20130117852 | Stute | May 2013 | A1 |
| 20130117855 | Kim et al. | May 2013 | A1 |
| 20130139264 | Brinkley et al. | May 2013 | A1 |
| 20130160125 | Likhachev et al. | Jun 2013 | A1 |
| 20130160127 | Jeong et al. | Jun 2013 | A1 |
| 20130160130 | Mendelev et al. | Jun 2013 | A1 |
| 20130160131 | Madou et al. | Jun 2013 | A1 |
| 20130167236 | Sick | Jun 2013 | A1 |
| 20130174214 | Duncan | Jul 2013 | A1 |
| 20130185789 | Hagiwara et al. | Jul 2013 | A1 |
| 20130185795 | Winn et al. | Jul 2013 | A1 |
| 20130185798 | Saunders et al. | Jul 2013 | A1 |
| 20130191915 | Antonakakis et al. | Jul 2013 | A1 |
| 20130196649 | Paddon et al. | Aug 2013 | A1 |
| 20130227691 | Aziz et al. | Aug 2013 | A1 |
| 20130246370 | Bartram et al. | Sep 2013 | A1 |
| 20130247186 | LeMasters | Sep 2013 | A1 |
| 20130263260 | Mahaffey et al. | Oct 2013 | A1 |
| 20130291109 | Staniford et al. | Oct 2013 | A1 |
| 20130298243 | Kumar et al. | Nov 2013 | A1 |
| 20130318038 | Shiffer et al. | Nov 2013 | A1 |
| 20130318073 | Shiffer et al. | Nov 2013 | A1 |
| 20130325791 | Shiffer et al. | Dec 2013 | A1 |
| 20130325792 | Shiffer et al. | Dec 2013 | A1 |
| 20130325871 | Shiffer et al. | Dec 2013 | A1 |
| 20130325872 | Shiffer et al. | Dec 2013 | A1 |
| 20140032875 | Butler | Jan 2014 | A1 |
| 20140053260 | Gupta et al. | Feb 2014 | A1 |
| 20140053261 | Gupta et al. | Feb 2014 | A1 |
| 20140130158 | Wang et al. | May 2014 | A1 |
| 20140137180 | Lukacs et al. | May 2014 | A1 |
| 20140169762 | Ryu | Jun 2014 | A1 |
| 20140179360 | Jackson et al. | Jun 2014 | A1 |
| 20140181131 | Ross | Jun 2014 | A1 |
| 20140189687 | Jung et al. | Jul 2014 | A1 |
| 20140189866 | Shiffer et al. | Jul 2014 | A1 |
| 20140189882 | Jung et al. | Jul 2014 | A1 |
| 20140237600 | Silberman et al. | Aug 2014 | A1 |
| 20140280245 | Wilson | Sep 2014 | A1 |
| 20140283037 | Sikorski et al. | Sep 2014 | A1 |
| 20140283063 | Thompson et al. | Sep 2014 | A1 |
| 20140328204 | Klotsche et al. | Nov 2014 | A1 |
| 20140337836 | Ismael | Nov 2014 | A1 |
| 20140344926 | Cunningham et al. | Nov 2014 | A1 |
| 20140351935 | Shao et al. | Nov 2014 | A1 |
| 20140380473 | Bu et al. | Dec 2014 | A1 |
| 20140380474 | Paithane et al. | Dec 2014 | A1 |
| 20150007250 | Dicato, Jr. | Jan 2015 | A1 |
| 20150007312 | Pidathala et al. | Jan 2015 | A1 |
| 20150096018 | Mircescu | Apr 2015 | A1 |
| 20150096023 | Mesdaq et al. | Apr 2015 | A1 |
| 20150096024 | Haq et al. | Apr 2015 | A1 |
| 20150096025 | Ismael | Apr 2015 | A1 |
| 20150180886 | Staniford et al. | Jun 2015 | A1 |
| 20150186645 | Aziz et al. | Jul 2015 | A1 |
| 20150199513 | Ismael et al. | Jul 2015 | A1 |
| 20150199531 | Ismael et al. | Jul 2015 | A1 |
| 20150199532 | Ismael et al. | Jul 2015 | A1 |
| 20150220735 | Paithane et al. | Aug 2015 | A1 |
| 20150372980 | Eyada | Dec 2015 | A1 |
| 20160004869 | Ismael et al. | Jan 2016 | A1 |
| 20160006756 | Ismael et al. | Jan 2016 | A1 |
| 20160044000 | Cunningham | Feb 2016 | A1 |
| 20160127393 | Aziz et al. | May 2016 | A1 |
| 20160191547 | Zafar et al. | Jun 2016 | A1 |
| 20160191550 | Ismael et al. | Jun 2016 | A1 |
| 20160261612 | Mesdaq et al. | Sep 2016 | A1 |
| 20160285914 | Singh et al. | Sep 2016 | A1 |
| 20160301703 | Aziz | Oct 2016 | A1 |
| 20160335110 | Paithane et al. | Nov 2016 | A1 |
| 20170083703 | Abbasi et al. | Mar 2017 | A1 |
| 20180013770 | Ismael | Jan 2018 | A1 |
| 20180048660 | Paithane et al. | Feb 2018 | A1 |
| 20180121316 | Ismael et al. | May 2018 | A1 |
| 20180288077 | Siddiqui et al. | Oct 2018 | A1 |
| Number | Date | Country |
|---|---|---|
| 2439806 | Jan 2008 | GB |
| 2490431 | Oct 2012 | GB |
| 0223805 | Mar 2002 | WO |
| 0206928 | Nov 2003 | WO |
| 2007117636 | Oct 2007 | WO |
| 2008041950 | Apr 2008 | WO |
| 2011084431 | Jul 2011 | WO |
| 2011112348 | Sep 2011 | WO |
| 2012075336 | Jun 2012 | WO |
| 2012145066 | Oct 2012 | WO |
| 2013067505 | May 2013 | WO |
| 2014057542 | Apr 2014 | WO |
| Entry |
|---|
| Thomas H. Ptacek, and Timothy N. Newsham , “Insertion, Evasion, and Denial of Service: Eluding Network Intrusion Detection”, Secure Networks, (“Ptacek”), (Jan. 1998). |
| U.S. Appl. No. 14/042,420, filed Sep. 30, 2013 Non-Final Office Action dated Jan. 14, 2015. |
| Venezia, Paul , “NetDetector Captures Intrusions”, InfoWorld Issue 27, (“Venezia”), (Jul. 14, 2003). |
| 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. |
| “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). |
| AltaVista Advanced Search Results. “attack vector identifier”. Http://www.altavista.com/web/results?ltag=ody&pg=aq&aqmode=aqa=Event+Orch- estrator . . . , (Accessed on Sep. 15, 2009). |
| AltaVista Advanced Search Results. “Event Orchestrator”. Http://www.altavista.com/web/results?ltag=ody&pg=aq&aqmode=aqa=Event+Orch- esrator . . . , (Accessed on Sep. 3, 2009). |
| Aura, Tuomas, “Scanning electronic documents for personally identifiable information”, Proceedings of the 5th ACM workshop on Privacy in electronic society. ACM, 2006. |
| Baecher, “The Nepenthes Platform: An Efficient Approach to collect Malware”, Springer-verlag Berlin Heidelberg, (2006), pp. 165-184. |
| Bayer, et al., “Dynamic Analysis of Malicious Code”, J Comput Virol, Springer-Verlag, France., (2006), pp. 67-77. |
| Boubalos, Chris , “extracting syslog data out of raw pcap dumps, seclists.org, Honeypots mailing list archives”, available at http://seclists.org/honeypots/2003/q2/319 (“Boubalos”), (Jun. 5, 2003). |
| Chaudet, C. , et al., “Optimal Positioning of Active and Passive Monitoring Devices”, International Conference on Emerging Networking Experiments and Technologies, Proceedings of the 2005 ACM Conference on Emerging Network Experiment and Technology, CoNEXT '05, Toulousse, France, (Oct. 2005), pp. 71-82. |
| Chen, P. M. and Noble, B. D., “When Virtual is Better Than Real, Department of Electrical Engineering and Computer Science”, University of Michigan (“Chen”). |
| Cisco, Configuring the Catalyst Switched Port Analyzer (SPAN) (“Cisco”), (1992-2003). |
| 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). |
| Goel, et al., Reconstructing System State for Intrusion Analysis, Apr. 2008 SIGOPS Operating Systems Review, vol. 42 Issue 3, pp. 21-28. |
| 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). |
| Kaeo, Merike , “Designing Network Security”, (“Kaeo”), (Nov. 2003). |
| Kim, H. , et al., “Autograph: Toward Automated, Distributed Worm Signature Detection”, Proceedings of the 13th USENIX Security Symposium (Security 2004), San Diego, (Aug. 2004), pp. 271-286. |
| King, Samuel T., et al., “Operating System Support for Virtual Machines”, (“King”) (2003). |
| Krasnyansky, Max , et al., Universal TUN/TAP driver, available at https://www.kernel.org/doc/Documentation/networking/tuntap.txt (2002) (“Krasnyansky”). |
| Kreibich, C. , et al., “Honeycomb-Creating Intrusion Detection Signatures Using Honeypots”, 2nd Workshop on Hot Topics in Networks (HotNets-11), Boston, USA, (2003). |
| Kristoff, J. , “Botnets, Detection and Mitigation: DNS-Based Techniques”, NU Security Day, (2005), 23 pages. |
| 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). |
| 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/US2014/055961 filde Sep. 16, 2014 International Search Report dated Jan. 5, 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). |
| 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. |
| 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. |
| Cisco “Intrusion Prevention for the Cisco ASA 5500-x Series” Data Sheet (2012). |
| 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. |
| 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. |
| 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. |
| Isohara, Takamasa, Keisuke Takemori, and Ayumu Kubota. “Kernel-based behavior analysis for android malware letection.” Computational intelligence and Security (CIS), 2011 Seventh International Conference on. IEEE, 2011. |
| 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. |
| 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. |
| 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]. |
| 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. |
| 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. |
| 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. |
| EP 14781744.9 filed Apr. 29, 2016 Office Action dated Aug. 29, 2017. |
| Didier Stevens, “Malicious PDF Documents Explained”, Security & Privacy, IEEE, IEEE Service Center, Los Alamitos, CA, US, vol. 9, No. 1, Jan. 1, 2011, pp. 80-82, XP011329453, ISSN: 1540-7993, DOI: 10.1109/MSP.2011.14. |
| Hiroshi Shinotsuka, Malware Authors Using New Techniques to Evade Automated Threat Analysis Systems, Oct. 26, 2012, http://www.symantec.com/connect/blogs/, pp. 1-4. |
| Khaled Salah et al: “Using Cloud Computing to Implement a Security Overlay Network”, Security & Privacy, IEEE, IEEE Service Center, Los Alamitos, CA, US, vol. 11, No. 1, Jan. 1, 2013 (Jan. 1, 2013). |
| Lastline Labs, The Threat of Evasive Malware, Feb. 25, 2013, Lastline Labs, pp. 1-8. |
| Vladimir Getov: “Security as a Service in Smart Clouds—Opportunities and Concerns”, Computer Software and Applications Conference (COMPSAC), 2012 IEEE 36TH Annual, IEEE, Jul. 16, 2012 (Jul. 16, 2012). |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 14042420 | Sep 2013 | US |
| Child | 14922030 | US |
