Embodiments of the disclosure related to the field of cybersecurity. More specifically, embodiments of the disclosure relate to a system for detecting and preventing ransomware attacks before files are affected.
Ransomware is a fast-growing threat and is often distributed through the Internet. Ransomware is a type of malicious software (malware) that locks a user out of their device, applications, and/or data. Ransomware may operate by encrypting or otherwise making inaccessible files on an endpoint computer and, typically, alerting a user of the endpoint to the fact that they will not be able to access their files until a ransom is collected. Often ransomware will operate as a background process without any user interaction. Ransomware may target specific filetypes and/or content of high value to elicit payment from the affected user. Some ransomware may irreversibly render files inaccessible, despite ransom payment, making data irretrievable.
For example, ransomware may be downloaded and/or installed on a computer by “drive-by” download (silently installed in the background without user interaction while downloading webpages). Once installed on the endpoint, the user may have no further interaction with the ransomware until the contents of the computer are encrypted as part of the cyber-attack. At least once important data on the endpoint is encrypted, the endpoint user may be alerted by the ransomware to the potential for loss unless a ransom is paid.
Current solutions for ransomware prevention rely on detecting ransomware manipulating “honey files” and preventing that modification. Unfortunately, in the process ransomware is typically able to encrypt at least few of the legitimate files on the system. Moreover, these solutions are highly resource intensive and may fail to detect ransomware designed to ignore honey files. An improved ransomware detection system is needed to reliably protect endpoints from ransomware cyber-security attacks.
Embodiments of this disclosure are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings, in which like references indicate similar elements and in which:
A ransomware prevention technique, limiting the effectiveness of cyber-attacks which encrypt or otherwise render inaccessible files, is provided. The ransomware prevention technique is implemented by a prevention system which (i) identifies a file access attempt (e.g., modify, delete, and/or move) by a process on a protected endpoint, (ii) intercepts the attempted file access by the process, (iii) determines if the attempted file access is permissible, and (iv) if permissible, allows the process to access the file while, if the access is impermissible, prevents access unless otherwise approved by the endpoint user.
More specifically, the ransomware prevention technique embodied in a ransomware prevention system monitors a protected endpoint system for attempted file accesses to one or more files during the execution of a process. One or more requested file accesses by an object or a computer program processing an object (referred to as a “file access events”), during runtime triggers the operation of the ransomware prevention system. An attempted file access request may be directed to any of a variety of files and file locations. These may include (a) any or all the files residing on (or accessible from) the protected endpoint system, (b) a specific type of file located on (or accessible from) the protected endpoint system (e.g., non-system files, excel files, etc.), and/or (c) files designated by the user or administrator of the protected endpoint, e.g., via a user interface. A monitoring logic is provided by the ransomware protection system to detect requests for file access (i.e. file access events). The ransomware prevention system (RPS) may operate by adapting or otherwise provisioning an application (e.g., Microsoft Word, etc.), with monitoring logic, and/or adapting or otherwise provisioning the operating system of the protected endpoint system to generate an analyzable attempted file access event (also referred to as a “file access event”) when triggered by a process during execution. In some embodiments, a processor-executable computer program (i.e. agent) may monitor one or more processes executing on the protected system and generate analyzable events when requests for file access are attempted by the process. A file access event may be generated in response to, but not limited to, an attempted file access, file modification, file move, and/or file delete of a protected file. In some embodiments, an attempted file access may further include attempted file accesses to create an incremental backup and/or “snapshot”. When the monitoring logic generates an analyzable event, the analyzable event is provided to a ransomware protection application for analysis.
When the ransomware prevention application (RPA) of the RPS generates an attempted file access event (i.e. an “analyzable event”), the RPA may generate an indicator to determine if the analyzable event is associated with known malicious (or benign) event indicators (i.e. a “known event indicator”). A known event indicator may be associated with at least a “block” (i.e. a blacklist) or “allow” (i.e. a whitelist) determination, the associated determination provided to a protection logic. The known event indicator and the respective determinations may be stored in an indicator store of the RPA. If the analyzable event is not associated with a known event indicator, the RPA may provide the analyzable event to a heuristics engine for further analysis.
The heuristics engine analyzes received characteristics and behaviors (collectively, “features”) associated with the analyzable event for suspiciousness. Features associated with the analyzable event may include, for example, the characteristics of the attempted file access event (e.g., filename and/or path of the file, the ID of the process requesting file access, etc.) as well as behaviors associated with the attempted file access event (e.g. file access request generated in response to a Graphical User Interface, “GUI”, interaction, processes requesting access to a several types of files, etc.) and behaviors related to the genealogy of the process requesting file access. The heuristics engine may examine the features associated with the analyzable event. The heuristics engine may also analyze objects associated with the received file access event, if available. If the analyzable event is determined to be suspicious by the heuristics engine, the engine may issue an alert to the user of the protected system and/or the security administrator of the system to take affirmative action to permit the process to proceed (or not) or, in some embodiments, provide an object associated with the analyzable event to a malware analysis system for further processing to determine if the object is malicious and its processing by the endpoint not permissible. In some embodiments, individual analyzable events may be analyzed for suspiciousness based on the features associated with the event while in alternative embodiments a plurality of analyzable events may be combined and the aggregated features of the combined analyzable events classified as suspicious by the classification engine.
The classification engine may receive from the heuristics engine the features detected and generate a classification of the analyzable event as being associated with “ransomware”, “suspicious”, or “benign”. The classification engine may generate the classification in response to determining a correlation of the features with known features of ransomware. The correlation of the features may be represented as a score, which when exceeding a first threshold may cause the event to be classified as being associated with ransomware based on machine learning and experiential knowledge. In some embodiments if the score does not exceed a second threshold, the analyzable event may be determined to be benign. The first and/or second threshold may be static (e.g., “factory-set”) or modifiable (i.e. in response to settings, endpoint user, etc.). In some embodiments the first and/or second threshold may be modifiable in response to an endpoint user role. If the classification engine determines that the analyzable event is associated with ransomware, the protection logic may be instructed to prevent further operations associated with the analyzable event (i.e. continued execution of the process associated with the analyzable event) while the reporting engine is instructed to report to a security administrator the ransomware event. When an analyzable event is determined to be associated with ransomware, the indicators associated with that analyzable event are stored in an indicator store of the RPA as known indicators associated with malware and/or ransomware. If the classification engine cannot determine the analyzable event is associated with ransomware or is benign (i.e. the ransomware score does not exceed the first threshold or second threshold), the analyzable event may be classified as “suspicious”.
In response to determining the analyzable event is suspicious the RPA may, in some embodiments, associate the analyzable event with a permissible operation and allow the operation to proceed; otherwise if the operation is not permissible, the operation may be prevented and an alert for the user and security administrator are generated and issued. In some embodiments, the RPA may enable the user to permit the operation for a suspicious analyzable event not previously known to be permissible via a user interaction with a user interface. If a suspicious analyzable event is deemed by the user to be permissible, its operation will be allowed and indicators associated with the analyzable event will be stored in the known indicator store and associated with benign (if the analyzable event is impermissible the indicator for the analyzable event will be associated with malware and/or ransomware in the known indicator store) events. The known indicator store may be based on characteristics of the protected file (e.g. file type). In some embodiments, in response to a suspiciousness determination of the analyzable event, the RPA may alert the user to determine whether the process generating the analyzable event is allowed to continue. If an attempted file access event is not determined to be permissible, the process initiating the analyzable event cannot receive the requested access to the file and the process will not continue execution (execution will be ended by the protection logic of the RPS).
The ransomware prevention technique described herein prevents ransomware cyberattacks by preventing their file operations. By analyzing features associated with attempted file accesses the system is able to identify suspicious operations and prevent ransomware from effecting operation on the protected system.
Terminology
In the following description, certain terminology is used to describe features of the invention. For example, in certain situations, the term “logic” may be representative of hardware, firmware and/or software that is configured to perform one or more functions. As hardware, logic may include circuitry having data processing or storage functionality. Examples of such circuitry may include, but are not limited or restricted to a microprocessor, one or more processor cores, a programmable gate array, a microcontroller, a controller, an application specific integrated circuit, wireless receiver, transmitter and/or transceiver circuitry, semiconductor memory, or combinatorial logic.
The term “process” may include an instance of a computer program (e.g., a collection of instructions, also referred to herein as an application). In one embodiment, the process may be included of one or more threads executing concurrently (e.g., each thread may be executing the same or a different instruction concurrently).
The term “processing” may include execution of a binary or script, or launching an application in which an object is processed, wherein launching should be interpreted as placing the application in an open state and, in some implementations, performing simulations of actions typical of human interactions with the application. For example, the application, an internet browsing application, may be processed such that the application is opened and actions such as “visiting” a website, downloading website pages, scrolling the website page, and activating a link from the website are performed.
The term “object” generally refers to a collection of data, whether in transit (e.g., over a network) or at rest (e.g., stored), often having a logical structure or organization that enables it to be categorized or typed for purposes of analysis. During analysis, for example, the object may exhibit a set of expected and/or unexpected features and, during processing, a set of expected and/or unexpected behaviors, which may evidence the presence of malware and potentially allow the object to be categorized or typed as malware. For example, an unexpected behavior of an object may include the generation of additional objects by an object being processed. In one embodiment, an object may include a binary file that may be executed within a virtual machine. Herein, the terms “binary file” and “binary” will be used interchangeably.
The term “file” is used in a broad sense to refer to a set or collection of data, information or other content used with a computer program. A file may be accessed, opened, stored, manipulated or otherwise processed as a single entity, object or unit. A file may contain other files, and may contain related or unrelated contents or no contents at all. A file may also have a logical format, and/or be part of a file system having a logical structure or organization of plural files. Files may have a name, sometimes called simply the “filename,” and often appended properties or other metadata. There are many types of files, such as data files, text files, program files, and directory files. A file may be generated by a user of a computing device or generated by the computing device. Access and/or operations on a file may be mediated by one or more applications and/or the operating system of a computing device. A filesystem may organize the files of the computing device of a storage device. The filesystem may enable tracking of files and enable access of those files. A filesystem may also enable operations on a file. In some embodiments the operations on the file may include file creation, file modification, file opening, file reading, file writing, file closing, and file deletion.
The term “feature” may be understood to refer, collectively, to characteristics of an object that may be detected statically and behaviors manifested in response to the processing of the object. Characteristics may include information about the object captured without requiring execution or “running” of the object. For example, characteristics may include metadata associated with the object, including, anomalous formatting or structuring of the object. Features may also include behaviors, where behaviors include information about the object and its activities captured during its execution or processing. Behaviors may include, but are not limited to, attempted outbound communications over a network connection or with other processes (e.g. the operating system, etc.), patterns of activity or inactivity, and/or attempts to access system resources.
The term “network device” may be construed as any intelligent electronic device with the capability of connecting to a network. Such a network may be a public network such as the internet or a private network such as a wireless data telecommunication network, wide area network, a type of local area network (LAN), or a combination of networks. Examples of a network device may include, but are not limited or restricted to a network appliance, laptop, mobile phone, or server.
The term “ransomware,” as described above, is a type of malicious software that is used to encrypt or otherwise prevent access to important information on a computer until payment (or “ransom”) is paid to the ransomware owner. Ransomware may rely on a network connection to receive confirmation of ransom payment while some ransomware requires entry of an unlock code that may be provided by the Ransomware owner once the ransom is paid. Ransomware may be time bound, deleting content from the computer or rendering the content permanently inaccessible if ransom is not paid before expiration of the ransomware.
Lastly, the terms “or” and “and/or” as used herein are to be interpreted as inclusive or meaning any one or any combination. Therefore, “A, B or C” or “A, B and/or C” mean “any of the following: A; B; C; A and B; A and C; B and C; A, B and C.” An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive.
Ransomware Protection System
Generally speaking, the ransomware prevention system (RPS) 100 may be implemented as one or more components executed by at least one electronic computing device where each includes physical hardware comprising hardware processor(s), network interface(s), a memory, and a system interconnect as shown in
The RPA 130 may include an indicator scanner 140, an indicator store 145, a classification engine, a protection logic 175 and a reporting engine 180. In some embodiments, the RPA 130 may further include a heuristics engine 150 and/or a protection logic 175. The RPA receives from the monitoring logic, of the RPS 100, operating on a user-operated electronic computing device (i.e. an endpoint), one or more file access events (“analyzable events”). The RPA 130 receives analyzable events, the analyzable events including features associated with the attempted file access for analysis to determine if the analyzable event is associated with a ransomware cyber-attack. The RPA 130 provides the analyzable event, and associated features, the indicator scanner 140 for analysis.
The indicator scanner 140 receives features associated with attempted file accesses generates indicators associated with the features, and compares the features with known indicators (e.g., known indicators may comprise known characteristics and/or known behaviors of malware) to determine if the analyzable event associated with the indicators are known to be associated ransomware (or malicious behavior) or are known to be benign. The received analyzable events may be provided by the monitoring logic of the RPS 100. The monitoring logic may be adapted to operate with the operating system of the RPS 100, embodied as operating system monitoring logic 115, and/or in one or more applications 120, embodied as application monitoring logic 125. The known indicators are each associated with a previously encountered analyzable event known to be “benign” or “ransomware”. In some embodiments, the indicator scanner 140 may be configured with a whitelist (indicators determined to be benign) and a blacklist (indicators determined to be associated with ransomware cyber-attacks).
The heuristics engine 150 applies heuristics and/or probability analysis to determine if the received file access events might include the hallmarks of ransomware. The heuristics engine 150 applies heuristics to the features of received events such as patterns of access, processing, etc. to determine a probability of being associated with ransomware. In some embodiments, the heuristics engine 150 may analyze the object indicated as a source of the file access event in coordination with a local malicious object analysis engine (not shown) or in coordination with remotely located malicious object analysis engines. The malicious object analysis engines may be located within an enterprise proprietary network or via an internet connect cloud network. The RPS 100 may provide to the malicious object analysis engine an object associated with the suspicious analyzable event and receive information associated with the maliciousness of the object. In some embodiments, the RPS 100 may receive an indicator associated with the classification to be retained in the indicator store 145. In some embodiments, the RPA 130 may prevent operation of the analyzable event until a response is received from the malicious object analysis engine.
When the determination of maliciousness of the attempted file access request is to be made by a separate service (i.e. a malicious object analysis engine), the RPA may temporarily determine the process requesting the attempted file access is to be treated as malicious (with the determination associated with features of the attempted file access event not stored in the known indicator store) and activate the protection logic, ending further execution of the process by the endpoint. Upon receipt from the separate service of an positive determination, by the separate malicious object analysis engine, of maliciousness of the process related to the attempted file access event, the indicators associated with the event are stored in the known indicator store in association with the determination and execution of the process continues to be blocked.
The heuristics engine 150 may be adapted to perform comparisons of an event and/or feature patterns under analysis against one or more pre-stored (e.g., pre-configured and/or predetermined) attack patterns stored in memory. The heuristics engine 150 may also be adapted to identify deviations in user file access patterns, the deviations determined through observation of user practices and experiential learning. Identified deviations from patterns of user file access are often a feature of ransomware cyber-attacks. The heuristics engine 150 may, in response to identifying patterns associated with ransomware generate a score indicative, to some level of probability, often well less than 100%, that the pattern is indicative of ransomware. The heuristics engine 150 may include a scoring logic to correlate one or more features, patterns of features of analyzable events associated with potential ransomware with known ransomware to generate a ransomware score. The generated ransomware score may be provided to the classification engine 160 for classification of the file access event as being associated with “ransomware” or “benign”.
The classification engine 160 receives from the heuristics engine 150 information associated with a likelihood of the analyzable event being associated with ransomware. Where the classification engine 160 receives from the heuristics engine 150 a correlation of the analyzable event and features with known ransomware, the classification engine may determine a score by weighting each correlation with each feature separately, and combining the separate scores using weights based on the experiential knowledge of the system. The classification engine 160 may be configured to use the scoring information provided by the heuristics engine 150 to classify an analyzable event as being associated with ransomware or benign. In one embodiment, when the score is above a first threshold, the classification engine 160 may generate an alert that the analyzable event is associated with ransomware. When the score is greater than a second threshold but lower than the first threshold, the analyzable event may be provided to the user interaction logic 170 for a user (or security administrator) to determine if the analyzable event should be permitted to continue operation (the analyzable event should not be associated with malware and/or ransomware). When the score is less than the second threshold, the classification engine 160 may determine no further analysis is needed and the process generating the analyzable event may continue operation (i.e., the event is benign). The one or more thresholds may be fixed, modified by a security administrator, and/or modified based on operating conditions of the electronic device (for example, if an electronic device is experiencing repeated events or attempted access or executions by certain processes sharing a set of features, etc.). In some embodiments, the classification engine 160 may use only the correlation information provided by the heuristics engine 150.
The protection logic 175 receives a determination, from the classification engine 160 and/or the optional user interaction engine 170, of a ransomware cyber-attack in response to a file access event. The protection logic 175 may coordinate with corresponding protection logic adapted for the operating system 110 or in one or more application(s) 120. In some embodiments, the protection logic may coordinate the respective monitoring logic of the operating system 110 or of the one or more application(s) 120 to prevent the continued operation of the ransomware process. The protection logic 175 may direct the operating system 110 to end a process determined to be associated with a ransomware cyber-attack by invoking a system function. In some embodiments, the protection logic 175 may be configured to intercept system or application functions associated with ransomware cyber-attack and block their continued processing. The reporting engine 180 is adapted to receive information from the classification engine 160 and generate alerts that identify to a user, administrator, and/or a security analyst the likelihood of a ransomware cyber-attack associated with the file access event (also known as an analyzable event). In some embodiments, the alert may identify the object associated the file access event which has been associated with the ransomware cyber-attack. Other additional information regarding the ransomware object may optionally be included in the alerts. For example, an alert may include information associated with the affected user, information associated with the object, and targeted files (based features associated with the file access event).
The user interaction logic 170 provides for and receives user input by the user of the endpoint device. The user input received by the user interaction logic 170 may include instructions to permit, or not, the file access prompted by the process. The user interaction logic may present to the user of the endpoint device the capability to permit (or not) the file access on a “one-time” basis, on a continuous basis, and/or for specifically identified files or sets of files bearing certain characteristics (e.g. a shared filetype, creator, directory, etc.). The availability of certain capabilities to permit (or not) may be limited based on information received by the heuristics engine indicating a level of suspiciousness. In some embodiments, endpoint device user instructed permission grants may be provided to the reporting engine 180 and an alert issued to the system administrator. If an end endpoint device user does not permit file access permission to a process, the instruction is provided to the protection logic 175.
The RPA 130 operates by coordinating with monitoring logic of the endpoint device configured to monitoring the access of files on the endpoint (i.e. the protected device). File accesses on the endpoint may be related to a legitimate process (i.e. a user initiated process or an automatic and/or scheduled process effected during the normal operation of the endpoint) or an illegitimate process (i.e. operating to effect otherwise unsanctioned modifications to files on the system). The operating system monitoring logic 115, application monitoring logic 125, and ransomware protection application 130 may be included in the application during activation of the ransomware protection system 100. The application monitoring logic 125 may be configured to detect attempted file accesses by an application 120 and coordinate with the RPA 130 via communication of an event. The application monitoring logic 125 may detect attempted file accesses by monitoring the functions of the application (i.e. application programming interface (API) function calls, operating system function calls, etc.) during execution. If the application monitoring logic 125 detects an attempted access via a monitored function the monitoring logic generates an event, including features associated with the application 120 and its attempted file access, and issues the event to the RPA 130. Similarly, the operating system monitoring logic 115 monitors the processes associated with the execution of the operating system 110. The operating system monitoring logic 115 is configured to detect attempted file accesses by monitoring operating system function calls. The operating system monitoring logic 115, adapted to coordinate with the operating system 110, may collect information associated with the operating system function calls monitored by the monitoring logic (i.e. calling process, associated processes, owner of the process, function arguments, etc.).
The RPA coordinates with the monitoring logic of the electronic device (endpoint) to determine if illegitimate processes are operating on a file of the device. For example, in some embodiments, an operating system monitoring logic 115 is operates with the operating system 120 of the endpoint. By coordinating with the operating system 110 via the operating system monitoring logic 115, the RPA 130 may be alerted to a process attempting to access an operating system file (also referred to as a “system file”) of the endpoint. Similarly, if an application is running on the endpoint and attempts to access a file, application monitoring logic 125 embedded in the application may be activated.
The operating system monitoring logic 115 and the application monitoring logic 125 of the ransomware protection system 100 identify behaviors (i.e. “observed behaviors”) associated with the processing of an object which initiates a file access event (i.e. features associated with executing a process). The monitoring logics, in some embodiments, may also identify characteristics associated with the processing of the object initiating the file access event. The observed behaviors and identified characteristics are features associated with the file access event. During classification by the classification engine 160, features may be used to classify the even as part of a ransomware cyber-attack.
In some embodiments, the monitoring logic may alternatively or additionally, operate at a different level of the software stack—e.g. at a hypervisor level in a virtual runtime environment—to intercept or capture such requests for file access (which generate analyzable events). More specifically, an executing process may send an API call containing a file access request, which becomes an operating system call, which in some embodiments may become a hypercall. Detection of the file access event at the hypervisor level may enable the implementation of the RPA in a secure analysis environment for such embodiments.
The application monitoring logic 125 monitor the operation of the one or more applications 120. In some embodiments, the application monitoring logic 125 and/or the operating system monitoring logic 115 intercepts behaviors associated with the event for analysis by the ransomware protection application 130.
During processing of an object on an electronic device protected by the ransomware protection system 100, the operating system monitoring logic 115 application monitoring logic 125 identify behaviors associated with the ransomware cyber-attacks (e.g. attempted file accesses, etc.). Signaling from application(s) 120 may be monitored through intercept points (sometimes referred to as “hooks”) to certain software calls (e.g., Application Programming Interface “API” call, library, procedure, function, or system call). The operations of the application may be monitored an intercept point (herein sometimes referred to as “instrumentation”) in code closely operating with the application to detect a certain type of activity, which may include an activity prompting a particular software call. In some embodiments, the monitoring logic may be embodied as hooks or instrumentation associated with calls to an operating system 120 function. The observed behavior associated with the processing of the object may be provided to the ransomware protection application 130 for analysis and determination if the feature is associated with a ransomware cyber-attack.
The RPA 130 may operate on an enterprise network 102 connected electronic device, such as a user operated endpoint computer. The RPA may communicate through the enterprise network 102 via a communication interface 305. The communication interface 305 may connect to the enterprise network 102 via a direct network interface or indirectly via a virtual private network. The enterprise network 102 may connected to the endpoint ransomware protection 100 via a communication interface 305. The communication interface 305 may connect to the enterprise network 102 via a direct network interface or indirectly via a virtual private network.
Referring now to
In step 230, the indicator scanner 140 receives the event and associated features for further analysis (i.e. the event and associated features having been allowed for further analysis by step 225. The indicator scanner 140 analyzes the received event and features and determines if they match an indicator associated with a known benign and/or ransomware process. In some embodiments the indicator scanner 140 may coordinate with an indicator store 145, the indicator store is communicatively coupled to the indicator scanner 140. In some embodiments, the indicator store 145 may be updated with updated indicators associated known benign and/or ransomware by coordinating communication via the communication interface 305 of the RPA 100. The communication interface 305 may be used to update the indicator store 145 via a connection to an enterprise network 102. In some embodiments the indicator store 145 may be modified in response to a determination of ransomware by the classification engine 160, the indicator store generating a new indicator in response to the classification and storing the generated indicator. If the indicator scanner 140 determines that the event and/or features detected by the monitoring logics is associated with a known indicator in step 235, the method proceeds to step 240. If the indicator scanner 140 does not determine the event and/or features are known (i.e. not associated with a known benign or ransomware sample) the system proceeds to step 250. In some embodiments the indicator scanner 140 may provide a determination of whether or not the event and/or features are associated with a known benign or ransomware event to the classification engine 160 of the RPA 130.
In step 240, the indicator scanner 140 relays to the classification engine if the known association (i.e. benign or ransomware) is permissible. If the indicator scanner determines that the event and/or features are not permissible the indicator scanner will indicate to the protection logic 175 the associated event should be prevented in step 245. Alternatively, if the indicator scanner determines the event and/or features are permissible, the event will be permitted proceed without further intervention in step 270. For example, if the indicator scanner identifies the features of the file access event detected by the monitoring logics to be associated with known ransomware the indicator scanner may determine, in step 240, that the file access event is not permissible and prevent its further processing in step 245. In some embodiments, the protection logic may receive from the classification engine 160 a determination as to whether or not the event and/or features are permissible or should be prevented by the protection logic 175 in response to determination of association with an indicator by the indicator scanner 140.
In step 245 the protection logic 175 of the RPA 130 prevents further processing of the ransomware. The protection logic 175 may coordinate with protection logic of the operating system 110 or in the application(s) 120 to effect prevention of ransomware on the RPS 100. The protection logic may indicate to the operating system 110 to stop processing objects associated with a ransomware process (as determined by the classification engine 160). In some embodiments the protection logic 175 may intercept and block processing of associated processes with a particular file access event. Similarly, the API functions of an application may be used to effect prevention by ending the requested file access. When the protection logic 175 is initiated, an alert is generated and issued to an administrator in step 275.
The process continues in step 275 wherein, the reporting engine 180 receives information from the protection logic 175 and generates alerts issued via the communication interface 305 that identify to an administrator the information associated with the detected ransomware cyber-attack. The information provided by the alert may include, in part, information associated with the originating process, as well as the user associated with the ransomware. Additional information regarding the ransomware object (the object associated with the identified ransomware cyber-attack) may optionally be included in the alerts. The reporting engine 180 may also provide connected network security systems with updated information regarding ransomware attacks and the associated file access event and/or features do not represent a ransomware cyber-attack, the reporting engine 180 may alert an administrator via an alert, while in alternative embodiments the reporting engine will not issue an alert. Once step 275 is complete, the ransomware prevention procedure concludes at step 280.
Meanwhile, if in step 240 the indicator scanner 140 determines that file access event is not permissible the protection logic is bypassed by the classification engine and the file access event is permitted to proceed and receive the required access without further intervention by the RPA 130. In some embodiments, the indicator scanner 140 provides the determination to the classification engine 160, while in other embodiments the classification engine receives a determination of whether or not the indicator is associated with ransomware or is benign and determines if the process is permissible.
If, in step 235, the indicator scanner 140 does not determine that the event and/or features associated with the event have been previously seen (i.e. are stored in the indicator store 145) the process proceeds to step 250 where the event and/or its associated features are provided to a heuristics engine 150 for further analysis. The heuristics engine 150 receives the event and/or features associated with the event and applies heuristics, rules or other conditions to the features to identify anomalous or unexpected behaviors which may be associated with ransomware. The analysis of the heuristics engine may yield an association between a set of one or more features and/or the event and a likelihood of association with ransomware. This likelihood of association with ransomware may be provided to a classification engine 160 for ransomware classification in step 260.
In step 260 the classification engine 160 receives from the heuristics engine 150 a likelihood of association with ransomware of the monitored features and/or file access event. In some embodiments the classification engine may receive an association with known ransomware or benign indicators from the indicator scanner 140. The classification engine 160 determines, based on the received information associated with each file access event whether or not it is associated with ransomware. If the classification engine 160 determines the event is associated with ransomware because the likelihood of association with ransomware exceeds a first threshold, the event will be determined and labelled as “ransomware” in step 265. Meanwhile, in some embodiments, if the likelihood of association with ransomware is below a second threshold, the event may be determined and labelled to be “benign”. If the association with ransomware is not labelled “benign” or “ransomware”, the event may, in some embodiments be labelled as “suspicious”. Suspicious events may be provided to a user interaction logic 170 for classification by a user. Based upon the user in response to an alert generated by the user interaction engine 170, the event will be classified as ransomware or benign. File access events determined to be benign by the classification engine 160 or the user interaction logic 170 may be permitted to receive access to the file requested in step 270. Meanwhile, in some embodiments, suspicious events may be prevented. Prevented events (including events classified as ransomware and in some embodiments, suspicious events) are provided to the reporting engine 180 in step 275 and in some embodiments to the protection logic 175. The process continues in step 275 as described above.
In step 220, the RPA receives from one or more monitoring logics the file access event and any associated features. The inventors recognize certain hallmarks of ransomware (or other malware rendering files maliciously encrypted, deleted or otherwise inaccessible) may be used as features for the classification of cyber-attacks as ransomware. The features may include, for example, details of the attempted file access (e.g., number of files accesses, file type of file(s) accessed, a process ID, process parent history, etc.), calling process history (i.e. is the calling process associated with another process, was the calling process directed by user-input, etc.), and/or whether there is an association with a user interface (i.e. did a user initiate the process via a Graphical User Interface which generated the analyzable event). Additional information which may be included as features are statistics related to the characteristics of the attempted file access event. The RPA 130 may use determine whether or not a file access event will be analyzed for ransomware protection based on the associated features of the object in step 220, alternatively, the procedure may proceed directly to step 230.
The processor(s) 310 is further coupled to a persistent storage 315 via a second transmission medium 313. According to one embodiment of the disclosure, the persistent storage 315 may include a communication interface logic 320, an operating system 110, an operating system monitoring logic 115, application(s) 120, application monitoring logic 125, a ransomware protection application 130, an indicator scanner 140, a classification engine 160, a protection logic 175 and a reporting engine 180, as well as an optional heuristics engine 150 and/or a user interaction engine 170. When implemented as hardware, one or more of these logic units could be implemented separately from each other.
In the foregoing description, the invention is described with reference to specific exemplary embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention as set forth in the appended claims.
This application claims the benefit of priority on U.S. Provisional Application No. 62/527,964 filed Jun. 30, 2017, the entire contents of which are incorporated by reference.
Number | Name | Date | Kind |
---|---|---|---|
4292580 | Ott et al. | Sep 1981 | A |
5175732 | Hendel et al. | Dec 1992 | A |
5319776 | Hile et al. | Jun 1994 | A |
5440723 | Arnold et al. | Aug 1995 | A |
5490249 | Miller | Feb 1996 | A |
5657473 | Killean et al. | Aug 1997 | A |
5802277 | Cowlard | Sep 1998 | A |
5842002 | Schnurer et al. | Nov 1998 | A |
5960170 | Chen et al. | Sep 1999 | A |
5978917 | Chi | Nov 1999 | A |
5983348 | Ji | Nov 1999 | A |
6088803 | Tso et al. | Jul 2000 | A |
6092194 | Touboul | Jul 2000 | A |
6094677 | Capek et al. | Jul 2000 | A |
6108799 | Boulay et al. | Aug 2000 | A |
6154844 | Touboul et al. | Nov 2000 | A |
6269330 | Cidon et al. | Jul 2001 | B1 |
6272641 | Ji | Aug 2001 | B1 |
6279113 | Vaidya | Aug 2001 | B1 |
6298445 | Shostack et al. | Oct 2001 | B1 |
6357008 | Nachenberg | Mar 2002 | B1 |
6424627 | Sorhaug et al. | Jul 2002 | B1 |
6442696 | Wray et al. | Aug 2002 | B1 |
6484315 | Ziese | Nov 2002 | B1 |
6487666 | Shanklin et al. | Nov 2002 | B1 |
6493756 | O'Brien et al. | Dec 2002 | B1 |
6550012 | Villa et al. | Apr 2003 | B1 |
6775657 | Baker | Aug 2004 | B1 |
6831893 | Ben Nun et al. | Dec 2004 | B1 |
6832367 | Choi et al. | Dec 2004 | B1 |
6895550 | Kanchirayappa et al. | May 2005 | B2 |
6898632 | Gordy et al. | May 2005 | B2 |
6907396 | Muttik et al. | Jun 2005 | B1 |
6941348 | Petry et al. | Sep 2005 | B2 |
6971097 | Wallman | Nov 2005 | B1 |
6981279 | Arnold et al. | Dec 2005 | B1 |
7007107 | Ivchenko et al. | Feb 2006 | B1 |
7028179 | Anderson et al. | Apr 2006 | B2 |
7043757 | Hoefelmeyer et al. | May 2006 | B2 |
7058822 | Edery et al. | Jun 2006 | B2 |
7069316 | Gryaznov | Jun 2006 | B1 |
7080407 | Zhao et al. | Jul 2006 | B1 |
7080408 | Pak et al. | Jul 2006 | B1 |
7093002 | Wolff et al. | Aug 2006 | B2 |
7093239 | van der Made | Aug 2006 | B1 |
7096498 | Judge | Aug 2006 | B2 |
7100201 | Izatt | Aug 2006 | B2 |
7107617 | Hursey et al. | Sep 2006 | B2 |
7159149 | Spiegel et al. | Jan 2007 | B2 |
7213260 | Judge | May 2007 | B2 |
7231667 | Jordan | Jun 2007 | B2 |
7240364 | Branscomb et al. | Jul 2007 | B1 |
7240368 | Roesch et al. | Jul 2007 | B1 |
7243371 | Kasper et al. | Jul 2007 | B1 |
7249175 | Donaldson | Jul 2007 | B1 |
7287278 | Liang | Oct 2007 | B2 |
7308716 | Danford et al. | Dec 2007 | B2 |
7328453 | Merkle, Jr. et al. | Feb 2008 | B2 |
7346486 | Ivancic et al. | Mar 2008 | B2 |
7356736 | Natvig | Apr 2008 | B2 |
7386888 | Liang et al. | Jun 2008 | B2 |
7392542 | Bucher | Jun 2008 | B2 |
7418729 | Szor | Aug 2008 | B2 |
7428300 | Drew et al. | Sep 2008 | B1 |
7441272 | Durham et al. | Oct 2008 | B2 |
7448084 | Apap et al. | Nov 2008 | B1 |
7458098 | Judge et al. | Nov 2008 | B2 |
7464404 | Carpenter et al. | Dec 2008 | B2 |
7464407 | Nakae et al. | Dec 2008 | B2 |
7467408 | O'Toole, Jr. | Dec 2008 | B1 |
7478428 | Thomlinson | Jan 2009 | B1 |
7480773 | Reed | Jan 2009 | B1 |
7487543 | Arnold et al. | Feb 2009 | B2 |
7496960 | Chen et al. | Feb 2009 | B1 |
7496961 | Zimmer et al. | Feb 2009 | B2 |
7519990 | Xie | Apr 2009 | B1 |
7523493 | Liang et al. | Apr 2009 | B2 |
7530104 | Thrower et al. | May 2009 | B1 |
7540025 | Tzadikario | May 2009 | B2 |
7546638 | Anderson et al. | Jun 2009 | B2 |
7565550 | Liang et al. | Jul 2009 | B2 |
7568233 | Szor et al. | Jul 2009 | B1 |
7584455 | Ball | Sep 2009 | B2 |
7603715 | Costa et al. | Oct 2009 | B2 |
7607171 | Marsden et al. | Oct 2009 | B1 |
7639714 | Stolfo et al. | Dec 2009 | B2 |
7644441 | Schmid et al. | Jan 2010 | B2 |
7657419 | van der Made | Feb 2010 | B2 |
7676841 | Sobchuk et al. | Mar 2010 | B2 |
7698548 | Shelest et al. | Apr 2010 | B2 |
7707633 | Danford et al. | Apr 2010 | B2 |
7712136 | Sprosts et al. | May 2010 | B2 |
7730011 | Deninger et al. | Jun 2010 | B1 |
7739740 | Nachenberg et al. | Jun 2010 | B1 |
7779463 | Stolfo et al. | Aug 2010 | B2 |
7784097 | Stolfo et al. | Aug 2010 | B1 |
7832008 | Kraemer | Nov 2010 | B1 |
7836502 | Zhao et al. | Nov 2010 | B1 |
7849506 | Dansey et al. | Dec 2010 | B1 |
7854007 | Sprosts et al. | Dec 2010 | B2 |
7869073 | Oshima | Jan 2011 | B2 |
7877803 | Enstone et al. | Jan 2011 | B2 |
7904959 | Sidiroglou et al. | Mar 2011 | B2 |
7908660 | Bahl | Mar 2011 | B2 |
7930738 | Petersen | Apr 2011 | B1 |
7937387 | Frazier et al. | May 2011 | B2 |
7937761 | Bennett | May 2011 | B1 |
7949849 | Lowe et al. | May 2011 | B2 |
7996556 | Raghavan et al. | Aug 2011 | B2 |
7996836 | McCorkendale et al. | Aug 2011 | B1 |
7996904 | Chiueh et al. | Aug 2011 | B1 |
7996905 | Arnold et al. | Aug 2011 | B2 |
8006305 | Aziz | Aug 2011 | B2 |
8010667 | Zhang et al. | Aug 2011 | B2 |
8020206 | Hubbard et al. | Sep 2011 | B2 |
8028338 | Schneider et al. | Sep 2011 | B1 |
8042184 | Batenin | Oct 2011 | B1 |
8045094 | Teragawa | Oct 2011 | B2 |
8045458 | Alperovitch et al. | Oct 2011 | B2 |
8069484 | McMillan et al. | Nov 2011 | B2 |
8087086 | Lai et al. | Dec 2011 | B1 |
8171553 | Aziz et al. | May 2012 | B2 |
8176049 | Deninger et al. | May 2012 | B2 |
8176480 | Spertus | May 2012 | B1 |
8201246 | Wu et al. | Jun 2012 | B1 |
8204984 | Aziz et al. | Jun 2012 | B1 |
8214905 | Doukhvalov et al. | Jul 2012 | B1 |
8220055 | Kennedy | Jul 2012 | B1 |
8225288 | Miller et al. | Jul 2012 | B2 |
8225373 | Kraemer | Jul 2012 | B2 |
8233882 | Rogel | Jul 2012 | B2 |
8234640 | Fitzgerald et al. | Jul 2012 | B1 |
8234709 | Viljoen et al. | Jul 2012 | B2 |
8239944 | Nachenberg et al. | Aug 2012 | B1 |
8260914 | Ranjan | Sep 2012 | B1 |
8266091 | Gubin et al. | Sep 2012 | B1 |
8286251 | Eker et al. | Oct 2012 | B2 |
8291499 | Aziz et al. | Oct 2012 | B2 |
8307435 | Mann et al. | Nov 2012 | B1 |
8307443 | Wang et al. | Nov 2012 | B2 |
8312545 | Tuvell et al. | Nov 2012 | B2 |
8321936 | Green et al. | Nov 2012 | B1 |
8321941 | Tuvell et al. | Nov 2012 | B2 |
8332571 | Edwards, Sr. | Dec 2012 | B1 |
8365286 | Poston | Jan 2013 | B2 |
8365297 | Parshin et al. | Jan 2013 | B1 |
8370938 | Daswani et al. | Feb 2013 | B1 |
8370939 | Zaitsev et al. | Feb 2013 | B2 |
8375444 | Aziz et al. | Feb 2013 | B2 |
8381299 | Stolfo et al. | Feb 2013 | B2 |
8402529 | Green et al. | Mar 2013 | B1 |
8464340 | Ahn et al. | Jun 2013 | B2 |
8479174 | Chiriac | Jul 2013 | B2 |
8479276 | Vaystikh et al. | Jul 2013 | B1 |
8479291 | Bodke | Jul 2013 | B1 |
8510827 | Leake et al. | Aug 2013 | B1 |
8510828 | Guo et al. | Aug 2013 | B1 |
8510842 | Amit et al. | Aug 2013 | B2 |
8516478 | Edwards et al. | Aug 2013 | B1 |
8516590 | Ranadive et al. | Aug 2013 | B1 |
8516593 | Aziz | Aug 2013 | B2 |
8522348 | Chen et al. | Aug 2013 | B2 |
8528086 | Aziz | Sep 2013 | B1 |
8533824 | Hutton et al. | Sep 2013 | B2 |
8539582 | Aziz et al. | Sep 2013 | B1 |
8549638 | Aziz | Oct 2013 | B2 |
8555391 | Demir et al. | Oct 2013 | B1 |
8561177 | Aziz et al. | Oct 2013 | B1 |
8566476 | Shiffer et al. | Oct 2013 | B2 |
8566946 | Aziz et al. | Oct 2013 | B1 |
8584094 | Dadhia et al. | Nov 2013 | B2 |
8584234 | Sobel et al. | Nov 2013 | B1 |
8584239 | Aziz et al. | Nov 2013 | B2 |
8595834 | Xie et al. | Nov 2013 | B2 |
8627476 | Satish et al. | Jan 2014 | B1 |
8635696 | Aziz | Jan 2014 | B1 |
8682054 | Xue et al. | Mar 2014 | B2 |
8682812 | Ranjan | Mar 2014 | B1 |
8689333 | Aziz | Apr 2014 | B2 |
8695096 | Zhang | Apr 2014 | B1 |
8713631 | Pavlyushchik | Apr 2014 | B1 |
8713681 | Silberman et al. | Apr 2014 | B2 |
8726392 | McCorkendale et al. | May 2014 | B1 |
8739280 | Chess et al. | May 2014 | B2 |
8776229 | Aziz | Jul 2014 | B1 |
8782792 | Bodke | Jul 2014 | B1 |
8789172 | Stolfo et al. | Jul 2014 | B2 |
8789178 | Kejriwal et al. | Jul 2014 | B2 |
8793278 | Frazier et al. | Jul 2014 | B2 |
8793787 | Ismael et al. | Jul 2014 | B2 |
8805947 | Kuzkin et al. | Aug 2014 | B1 |
8806647 | Daswani et al. | Aug 2014 | B1 |
8832829 | Manni 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 | Shifter et al. | Feb 2015 | B2 |
8984638 | Aziz et al. | Mar 2015 | B1 |
8990939 | Staniford et al. | Mar 2015 | B2 |
8990944 | Singh et al. | Mar 2015 | B1 |
8997219 | Staniford et al. | Mar 2015 | B2 |
9009822 | Ismael et al. | Apr 2015 | B1 |
9009823 | Ismael et al. | Apr 2015 | B1 |
9027135 | Aziz | May 2015 | B1 |
9071638 | Aziz et al. | Jun 2015 | B1 |
9104867 | Thioux et al. | Aug 2015 | B1 |
9106630 | Frazier et al. | Aug 2015 | B2 |
9106694 | Aziz et al. | Aug 2015 | B2 |
9118715 | Staniford et al. | Aug 2015 | B2 |
9159035 | Ismael et al. | Oct 2015 | B1 |
9171160 | Vincent et al. | Oct 2015 | B2 |
9176843 | Ismael et al. | Nov 2015 | B1 |
9189627 | Islam | Nov 2015 | B1 |
9195829 | Goradia et al. | Nov 2015 | B1 |
9197664 | Aziz et al. | Nov 2015 | B1 |
9223972 | Vincent et al. | Dec 2015 | B1 |
9225740 | Ismael et al. | Dec 2015 | B1 |
9241010 | Bennett et al. | Jan 2016 | B1 |
9251343 | Vincent et al. | Feb 2016 | B1 |
9262635 | Paithane et al. | Feb 2016 | B2 |
9268936 | Butler | Feb 2016 | B2 |
9275229 | LeMasters | Mar 2016 | B2 |
9282109 | Aziz et al. | Mar 2016 | B1 |
9292686 | Ismael et al. | Mar 2016 | B2 |
9294501 | Mesdaq et al. | Mar 2016 | B2 |
9300686 | Pidathala et al. | Mar 2016 | B2 |
9306960 | Aziz | Apr 2016 | B1 |
9306974 | Aziz et al. | Apr 2016 | B1 |
9311479 | Manni et al. | Apr 2016 | B1 |
9355247 | Thioux et al. | May 2016 | B1 |
9356944 | Aziz | May 2016 | B1 |
9363280 | Rivlin et al. | Jun 2016 | B1 |
9367681 | Ismael et al. | Jun 2016 | B1 |
9398028 | Karandikar et al. | Jul 2016 | B1 |
9413781 | Cunningham et al. | Aug 2016 | B2 |
9426071 | Caldejon et al. | Aug 2016 | B1 |
9430646 | Mushtaq et al. | Aug 2016 | B1 |
9432389 | Khalid et al. | Aug 2016 | B1 |
9438613 | Paithane et al. | Sep 2016 | B1 |
9438622 | Staniford et al. | Sep 2016 | B1 |
9438623 | Thioux et al. | Sep 2016 | B1 |
9459901 | Jung et al. | Oct 2016 | B2 |
9467460 | Otvagin et al. | Oct 2016 | B1 |
9483644 | Paithane et al. | Nov 2016 | B1 |
9495180 | Ismael | Nov 2016 | B2 |
9497213 | Thompson et al. | Nov 2016 | B2 |
9507935 | Ismael et al. | Nov 2016 | B2 |
9516057 | Aziz | Dec 2016 | B2 |
9519782 | Aziz et al. | Dec 2016 | B2 |
9536091 | Paithane et al. | Jan 2017 | B2 |
9537972 | Edwards et al. | Jan 2017 | B1 |
9560059 | Islam | Jan 2017 | B1 |
9565202 | Kindlund et al. | Feb 2017 | B1 |
9591015 | Amin et al. | Mar 2017 | B1 |
9591020 | Aziz | Mar 2017 | B1 |
9594904 | Jain et al. | Mar 2017 | B1 |
9594905 | Ismael et al. | Mar 2017 | B1 |
9594912 | Thioux et al. | Mar 2017 | B1 |
9609007 | Rivlin et al. | Mar 2017 | B1 |
9626509 | Khalid et al. | Apr 2017 | B1 |
9628498 | Aziz et al. | Apr 2017 | B1 |
9628507 | Haq et al. | Apr 2017 | B2 |
9633134 | Ross | Apr 2017 | B2 |
9635039 | Islam et al. | Apr 2017 | B1 |
9641546 | Manni et al. | May 2017 | B1 |
9654485 | Neumann | May 2017 | B1 |
9661009 | Karandikar et al. | May 2017 | B1 |
9661018 | Aziz | May 2017 | B1 |
9674298 | Edwards et al. | Jun 2017 | B1 |
9680862 | Ismael et al. | Jun 2017 | B2 |
9690606 | Ha et al. | Jun 2017 | B1 |
9690933 | Singh et al. | Jun 2017 | B1 |
9690935 | Shiffer et al. | Jun 2017 | B2 |
9690936 | Malik et al. | Jun 2017 | B1 |
9736179 | Ismael | Aug 2017 | B2 |
9740857 | Ismael et al. | Aug 2017 | B2 |
9747446 | Pidathala et al. | Aug 2017 | B1 |
9756074 | Aziz et al. | Sep 2017 | B2 |
9773112 | Rathor et al. | Sep 2017 | B1 |
9781144 | Otvagin et al. | Oct 2017 | B1 |
9787700 | Amin et al. | Oct 2017 | B1 |
9787706 | Otvagin et al. | Oct 2017 | B1 |
9792196 | Ismael et al. | Oct 2017 | B1 |
9824209 | Ismael et al. | Nov 2017 | B1 |
9824211 | Wilson | Nov 2017 | B2 |
9824216 | Khalid et al. | Nov 2017 | B1 |
9825976 | Gomez et al. | Nov 2017 | B1 |
9825989 | Mehra et al. | Nov 2017 | B1 |
9838408 | Karandikar et al. | Dec 2017 | B1 |
9838411 | Aziz | Dec 2017 | B1 |
9838416 | Aziz | Dec 2017 | B1 |
9838417 | Khalid et al. | Dec 2017 | B1 |
9846776 | Paithane et al. | Dec 2017 | B1 |
9876701 | Caldejon et al. | Jan 2018 | B1 |
9888016 | Amin et al. | Feb 2018 | B1 |
9888019 | Pidathala et al. | Feb 2018 | B1 |
9910988 | Vincent et al. | Mar 2018 | B1 |
9912644 | Cunningham | Mar 2018 | B2 |
9912681 | Ismael et al. | Mar 2018 | B1 |
9912684 | Aziz et al. | Mar 2018 | B1 |
9912691 | Mesdaq et al. | Mar 2018 | B2 |
9912698 | Thioux et al. | Mar 2018 | B1 |
9916440 | Paithane et al. | Mar 2018 | B1 |
9921978 | Chan et al. | Mar 2018 | B1 |
9934376 | Ismael | Apr 2018 | B1 |
9934381 | Kindlund et al. | Apr 2018 | B1 |
9946568 | Ismael et al. | Apr 2018 | B1 |
9954890 | Staniford et al. | Apr 2018 | B1 |
9973531 | Thioux | May 2018 | B1 |
10002252 | Ismael et al. | Jun 2018 | B2 |
10019338 | Goradia et al. | Jul 2018 | B1 |
10019573 | Silberman et al. | Jul 2018 | B2 |
10025691 | Ismael et al. | Jul 2018 | B1 |
10025927 | Khalid et al. | Jul 2018 | B1 |
10027689 | Rathor et al. | Jul 2018 | B1 |
10027690 | Aziz et al. | Jul 2018 | B2 |
10027696 | Rivlin et al. | Jul 2018 | B1 |
10033747 | Paithane et al. | Jul 2018 | B1 |
10033748 | Cunningham et al. | Jul 2018 | B1 |
10033753 | Islam et al. | Jul 2018 | B1 |
10033759 | Kabra et al. | Jul 2018 | B1 |
10050998 | Singh | Aug 2018 | B1 |
10068091 | Aziz et al. | Sep 2018 | B1 |
10075455 | Zafar et al. | Sep 2018 | B2 |
10083302 | Paithane et al. | Sep 2018 | B1 |
10084813 | Eyada | Sep 2018 | B2 |
10089461 | Ha et al. | Oct 2018 | B1 |
10097573 | Aziz | Oct 2018 | B1 |
10104102 | Neumann | Oct 2018 | B1 |
10108446 | Steinberg et al. | Oct 2018 | B1 |
10121000 | Rivlin et al. | Nov 2018 | B1 |
10122746 | Manni et al. | Nov 2018 | B1 |
10133863 | Bu et al. | Nov 2018 | B2 |
10133866 | Kumar et al. | Nov 2018 | B1 |
10146810 | Shiffer et al. | Dec 2018 | B2 |
10148693 | Singh et al. | Dec 2018 | B2 |
10165000 | Aziz et al. | Dec 2018 | B1 |
10169585 | Pilipenko et al. | Jan 2019 | B1 |
10176321 | Abbasi et al. | Jan 2019 | B2 |
10181029 | Ismael et al. | Jan 2019 | B1 |
10191861 | Steinberg et al. | Jan 2019 | B1 |
10192052 | Singh et al. | Jan 2019 | B1 |
10198574 | Thioux et al. | Feb 2019 | B1 |
10200384 | Mushtaq et al. | Feb 2019 | B1 |
10210329 | Malik et al. | Feb 2019 | B1 |
10216927 | Steinberg | Feb 2019 | B1 |
10218740 | Mesdaq et al. | Feb 2019 | B1 |
10242185 | Goradia | Mar 2019 | B1 |
20010005889 | Albrecht | Jun 2001 | A1 |
20010047326 | Broadbent et al. | Nov 2001 | A1 |
20020018903 | Kokubo et al. | Feb 2002 | A1 |
20020038430 | Edwards et al. | Mar 2002 | A1 |
20020091819 | Melchione et al. | Jul 2002 | A1 |
20020095607 | Lin-Hendel | Jul 2002 | A1 |
20020116627 | Tarbotton et al. | Aug 2002 | A1 |
20020144156 | Copeland | Oct 2002 | A1 |
20020162015 | Tang | Oct 2002 | A1 |
20020166063 | Lachman et al. | Nov 2002 | A1 |
20020169952 | DiSanto et al. | Nov 2002 | A1 |
20020184528 | Shevenell et al. | Dec 2002 | A1 |
20020188887 | Largman et al. | Dec 2002 | A1 |
20020194490 | Halperin et al. | Dec 2002 | A1 |
20030021728 | Sharpe et al. | Jan 2003 | A1 |
20030074578 | Ford et al. | Apr 2003 | A1 |
20030084318 | Schertz | May 2003 | A1 |
20030101381 | Mateev et al. | May 2003 | A1 |
20030115483 | Liang | Jun 2003 | A1 |
20030188190 | Aaron et al. | Oct 2003 | A1 |
20030191957 | Hypponen et al. | Oct 2003 | A1 |
20030200460 | Morota et al. | Oct 2003 | A1 |
20030212902 | van der Made | Nov 2003 | A1 |
20030229801 | Kouznetsov et al. | Dec 2003 | A1 |
20030237000 | Denton et al. | Dec 2003 | A1 |
20040003323 | Bennett et al. | Jan 2004 | A1 |
20040006473 | Mills et al. | Jan 2004 | A1 |
20040015712 | Szor | Jan 2004 | A1 |
20040019574 | Meng | 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 |
20060047665 | Neil | Mar 2006 | A1 |
20060070130 | Costea et al. | Mar 2006 | A1 |
20060075496 | Carpenter et al. | Apr 2006 | A1 |
20060095968 | Portolani et al. | May 2006 | A1 |
20060101516 | Sudaharan et al. | May 2006 | A1 |
20060101517 | Banzhof et al. | May 2006 | A1 |
20060117385 | Mester et al. | Jun 2006 | A1 |
20060123477 | Raghavan et al. | Jun 2006 | A1 |
20060143709 | Brooks et al. | Jun 2006 | A1 |
20060150249 | Gassen et al. | Jul 2006 | A1 |
20060161983 | Cothrell et al. | Jul 2006 | A1 |
20060161987 | Levy-Yurista | Jul 2006 | A1 |
20060161989 | Reshef et al. | Jul 2006 | A1 |
20060164199 | Glide et al. | Jul 2006 | A1 |
20060173992 | Weber et al. | Aug 2006 | A1 |
20060179147 | Tran et al. | Aug 2006 | A1 |
20060184632 | Marino et al. | Aug 2006 | A1 |
20060191010 | Benjamin | Aug 2006 | A1 |
20060221956 | Narayan et al. | Oct 2006 | A1 |
20060236393 | Kramer et al. | Oct 2006 | A1 |
20060242709 | Seinfeld et al. | Oct 2006 | A1 |
20060248519 | Jaeger et al. | Nov 2006 | A1 |
20060248582 | Panjwani et al. | Nov 2006 | A1 |
20060251104 | Koga | Nov 2006 | A1 |
20060288417 | Bookbinder et al. | Dec 2006 | A1 |
20070006288 | Mayfield et al. | Jan 2007 | A1 |
20070006313 | Porras et al. | Jan 2007 | A1 |
20070011174 | Takaragi et al. | Jan 2007 | A1 |
20070016951 | Piccard et al. | Jan 2007 | A1 |
20070019286 | Kikuchi | Jan 2007 | A1 |
20070033645 | Jones | Feb 2007 | A1 |
20070038943 | FitzGerald et al. | Feb 2007 | A1 |
20070064689 | Shin et al. | Mar 2007 | A1 |
20070074169 | Chess | Mar 2007 | A1 |
20070094730 | Bhikkaji et al. | Apr 2007 | A1 |
20070101435 | Konanka et al. | May 2007 | A1 |
20070128855 | Cho et al. | Jun 2007 | A1 |
20070142030 | Sinha et al. | Jun 2007 | A1 |
20070143827 | Nicodemus et al. | Jun 2007 | A1 |
20070156895 | Vuong | Jul 2007 | A1 |
20070157180 | Tillmann et al. | Jul 2007 | A1 |
20070157306 | Elrod et al. | Jul 2007 | A1 |
20070168988 | Eisner et al. | Jul 2007 | A1 |
20070171824 | Ruello et al. | Jul 2007 | A1 |
20070174915 | Gribble et al. | Jul 2007 | A1 |
20070192500 | Lum | Aug 2007 | A1 |
20070192858 | Lum | Aug 2007 | A1 |
20070198275 | Malden et al. | Aug 2007 | A1 |
20070208822 | Wang et al. | Sep 2007 | A1 |
20070220607 | Sprosts et al. | Sep 2007 | A1 |
20070240218 | Tuvell et al. | Oct 2007 | A1 |
20070240219 | Tuvell et al. | Oct 2007 | A1 |
20070240220 | Tuvell et al. | Oct 2007 | A1 |
20070240222 | Tuvell et al. | Oct 2007 | A1 |
20070250930 | Aziz et al. | Oct 2007 | A1 |
20070256132 | Oliphant | Nov 2007 | A2 |
20070271446 | Nakamura | Nov 2007 | A1 |
20080005782 | Aziz | Jan 2008 | A1 |
20080018122 | Zierler et al. | Jan 2008 | A1 |
20080028463 | Dagon et al. | Jan 2008 | A1 |
20080040710 | Chiriac | Feb 2008 | A1 |
20080046781 | Childs et al. | Feb 2008 | A1 |
20080066179 | Liu | Mar 2008 | A1 |
20080072326 | Danford et al. | Mar 2008 | A1 |
20080077793 | Tan et al. | Mar 2008 | A1 |
20080080518 | Hoeflin et al. | Apr 2008 | A1 |
20080086720 | Lekel | Apr 2008 | A1 |
20080098476 | Syversen | Apr 2008 | A1 |
20080120722 | Sima et al. | May 2008 | A1 |
20080134178 | Fitzgerald et al. | Jun 2008 | A1 |
20080134334 | Kim et al. | Jun 2008 | A1 |
20080141376 | Clausen et al. | Jun 2008 | A1 |
20080184367 | McMillan et al. | Jul 2008 | A1 |
20080184373 | Traut et al. | Jul 2008 | A1 |
20080189787 | Arnold et al. | Aug 2008 | A1 |
20080201778 | Guo et al. | Aug 2008 | A1 |
20080209557 | Herley et al. | Aug 2008 | A1 |
20080215742 | Goldszmidt et al. | Sep 2008 | A1 |
20080222729 | Chen et al. | Sep 2008 | A1 |
20080263665 | Ma et al. | Oct 2008 | A1 |
20080295172 | Bohacek | Nov 2008 | A1 |
20080301810 | Lehane et al. | Dec 2008 | A1 |
20080307524 | Singh et al. | Dec 2008 | A1 |
20080313738 | Enderby | Dec 2008 | A1 |
20080320594 | Jiang | Dec 2008 | A1 |
20090003317 | Kasralikar et al. | Jan 2009 | A1 |
20090007100 | Field et al. | Jan 2009 | A1 |
20090013408 | Schipka | Jan 2009 | A1 |
20090031423 | Liu et al. | Jan 2009 | A1 |
20090036111 | Danford et al. | Feb 2009 | A1 |
20090037835 | Goldman | Feb 2009 | A1 |
20090044024 | Oberheide et al. | Feb 2009 | A1 |
20090044274 | Budko | Feb 2009 | A1 |
20090064332 | Porras et al. | Mar 2009 | A1 |
20090077666 | Chen et al. | Mar 2009 | A1 |
20090083369 | Marmor | Mar 2009 | A1 |
20090083855 | Apap et al. | Mar 2009 | A1 |
20090089879 | Wang et al. | Apr 2009 | A1 |
20090094697 | Provos et al. | Apr 2009 | A1 |
20090113425 | Ports et al. | Apr 2009 | A1 |
20090125976 | Wassermann et al. | May 2009 | A1 |
20090126015 | Monastyrsky et al. | May 2009 | A1 |
20090126016 | Sobko et al. | May 2009 | A1 |
20090133125 | Choi et al. | May 2009 | A1 |
20090144823 | Lamastra et al. | Jun 2009 | A1 |
20090158430 | Borders | Jun 2009 | A1 |
20090172815 | Gu et al. | Jul 2009 | A1 |
20090187992 | Poston | Jul 2009 | A1 |
20090193293 | Stolfo et al. | Jul 2009 | A1 |
20090198651 | Shiffer et al. | Aug 2009 | A1 |
20090198670 | Shiffer et al. | Aug 2009 | A1 |
20090198689 | Frazier et al. | Aug 2009 | A1 |
20090199274 | Frazier et al. | Aug 2009 | A1 |
20090199296 | Xie et al. | Aug 2009 | A1 |
20090228233 | Anderson et al. | Sep 2009 | A1 |
20090241187 | Troyansky | Sep 2009 | A1 |
20090241190 | Todd et al. | Sep 2009 | A1 |
20090265692 | Godefroid et al. | Oct 2009 | A1 |
20090271867 | Zhang | Oct 2009 | A1 |
20090300415 | Zhang et al. | Dec 2009 | A1 |
20090300761 | Park et al. | Dec 2009 | A1 |
20090328185 | Berg et al. | Dec 2009 | A1 |
20090328221 | Blumfield et al. | Dec 2009 | A1 |
20100005146 | Drako et al. | Jan 2010 | A1 |
20100011205 | McKenna | Jan 2010 | A1 |
20100017546 | Poo et al. | Jan 2010 | A1 |
20100030996 | Butler, II | Feb 2010 | A1 |
20100031353 | Thomas et al. | Feb 2010 | A1 |
20100037314 | Perdisci et al. | Feb 2010 | A1 |
20100043073 | Kuwamura | Feb 2010 | A1 |
20100054278 | Stolfo et al. | Mar 2010 | A1 |
20100058474 | Hicks | Mar 2010 | A1 |
20100064044 | Nonoyama | Mar 2010 | A1 |
20100077481 | Polyakov et al. | Mar 2010 | A1 |
20100083376 | Pereira et al. | Apr 2010 | A1 |
20100115621 | Staniford et al. | May 2010 | A1 |
20100132038 | Zaitsev | May 2010 | A1 |
20100154056 | Smith et al. | Jun 2010 | A1 |
20100180344 | Malyshev et al. | Jul 2010 | A1 |
20100192223 | Ismael et al. | Jul 2010 | A1 |
20100220863 | Dupaquis et al. | Sep 2010 | A1 |
20100235831 | Dittmer | Sep 2010 | A1 |
20100251104 | Massand | Sep 2010 | A1 |
20100281102 | Chinta et al. | Nov 2010 | A1 |
20100281541 | Stolfo et al. | Nov 2010 | A1 |
20100281542 | Stolfo et al. | Nov 2010 | A1 |
20100287260 | Peterson et al. | Nov 2010 | A1 |
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 |
20110271343 | Kim | Nov 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 | Aug 2012 | A1 |
20120216280 | Zorn | Aug 2012 | A1 |
20120222121 | Staniford et al. | Aug 2012 | A1 |
20120255015 | Sahita et al. | Oct 2012 | A1 |
20120255017 | Sallam | Oct 2012 | A1 |
20120260342 | Dube et al. | Oct 2012 | A1 |
20120266244 | Green et al. | Oct 2012 | A1 |
20120278886 | Luna | Nov 2012 | A1 |
20120297489 | Dequevy | Nov 2012 | A1 |
20120330801 | McDougal et al. | Dec 2012 | A1 |
20120331553 | Aziz et al. | Dec 2012 | A1 |
20130014259 | Gribble et al. | Jan 2013 | A1 |
20130036472 | Aziz | Feb 2013 | A1 |
20130047257 | Aziz | Feb 2013 | A1 |
20130074185 | McDougal et al. | Mar 2013 | A1 |
20130086684 | Mohler | Apr 2013 | A1 |
20130097699 | Balupari et al. | Apr 2013 | A1 |
20130097706 | Titonis et al. | Apr 2013 | A1 |
20130111587 | Goel et al. | May 2013 | A1 |
20130117852 | Stute | May 2013 | A1 |
20130117855 | Kim et al. | May 2013 | A1 |
20130139264 | Brinkley et al. | May 2013 | A1 |
20130160125 | Likhachev et al. | Jun 2013 | A1 |
20130160127 | Jeong et al. | Jun 2013 | A1 |
20130160130 | Mendelev et al. | Jun 2013 | A1 |
20130160131 | Madou et al. | Jun 2013 | A1 |
20130167236 | Sick | Jun 2013 | A1 |
20130174214 | Duncan | Jul 2013 | A1 |
20130185789 | Hagiwara et al. | Jul 2013 | A1 |
20130185795 | Winn et al. | Jul 2013 | A1 |
20130185798 | Saunders et al. | Jul 2013 | A1 |
20130191915 | Antonakakis et al. | Jul 2013 | A1 |
20130196649 | Paddon et al. | Aug 2013 | A1 |
20130227691 | Aziz et al. | Aug 2013 | A1 |
20130246370 | Bartram et al. | Sep 2013 | A1 |
20130247186 | LeMasters | Sep 2013 | A1 |
20130263260 | Mahaffey et al. | Oct 2013 | A1 |
20130291109 | Staniford et al. | Oct 2013 | A1 |
20130298243 | Kumar et al. | Nov 2013 | A1 |
20130318038 | Shiffer et al. | Nov 2013 | A1 |
20130318073 | Shiffer et al. | Nov 2013 | A1 |
20130325791 | Shiffer et al. | Dec 2013 | A1 |
20130325792 | Shiffer et al. | Dec 2013 | A1 |
20130325871 | Shiffer et al. | Dec 2013 | A1 |
20130325872 | Shiffer et al. | Dec 2013 | A1 |
20140032875 | Butler | Jan 2014 | A1 |
20140053260 | Gupta et al. | Feb 2014 | A1 |
20140053261 | Gupta et al. | Feb 2014 | A1 |
20140130158 | Wang et al. | May 2014 | A1 |
20140137180 | Lukacs et al. | May 2014 | A1 |
20140169762 | Ryu | Jun 2014 | A1 |
20140179360 | Jackson et al. | Jun 2014 | A1 |
20140181131 | Ross | Jun 2014 | A1 |
20140189687 | Jung et al. | Jul 2014 | A1 |
20140189866 | Shiffer et al. | Jul 2014 | A1 |
20140189882 | Jung et al. | Jul 2014 | A1 |
20140237600 | Silberman et al. | Aug 2014 | A1 |
20140280245 | Wilson | Sep 2014 | A1 |
20140283037 | Sikorski et al. | Sep 2014 | A1 |
20140283063 | Thompson et al. | Sep 2014 | A1 |
20140328204 | Klotsche et al. | Nov 2014 | A1 |
20140337836 | Ismael | Nov 2014 | A1 |
20140344926 | Cunningham et al. | Nov 2014 | A1 |
20140351935 | Shao et al. | Nov 2014 | A1 |
20140380473 | Bu et al. | Dec 2014 | A1 |
20140380474 | Paithane et al. | Dec 2014 | A1 |
20150007312 | Pidathala et al. | Jan 2015 | A1 |
20150096022 | Vincent et al. | Apr 2015 | A1 |
20150096023 | Mesdaq et al. | Apr 2015 | A1 |
20150096024 | Haq et al. | Apr 2015 | A1 |
20150096025 | Ismael | Apr 2015 | A1 |
20150180886 | Staniford et al. | Jun 2015 | A1 |
20150186645 | Aziz et al. | Jul 2015 | A1 |
20150199513 | Ismael et al. | Jul 2015 | A1 |
20150199531 | Ismael et al. | Jul 2015 | A1 |
20150199532 | Ismael et al. | Jul 2015 | A1 |
20150220735 | Paithane et al. | Aug 2015 | A1 |
20150372980 | Eyada | Dec 2015 | A1 |
20160004869 | Ismael et al. | Jan 2016 | A1 |
20160006756 | Ismael et al. | Jan 2016 | A1 |
20160021141 | Liu | 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 |
20180034835 | Iwanir | Feb 2018 | A1 |
20180048660 | Paithane et al. | Feb 2018 | A1 |
20180077175 | DiValentin | Mar 2018 | A1 |
20180121316 | Ismael et al. | May 2018 | A1 |
20180239902 | Godard | Aug 2018 | A1 |
20180288077 | Siddiqui et al. | Oct 2018 | A1 |
20190387005 | Zawoad | Dec 2019 | A1 |
Number | Date | Country |
---|---|---|
2439806 | Jan 2008 | GB |
2490431 | Oct 2012 | GB |
0206928 | Jan 2002 | WO |
0223805 | Mar 2002 | WO |
2007117636 | Oct 2007 | WO |
2008041950 | Apr 2008 | WO |
2011084431 | Jul 2011 | WO |
2011112348 | Sep 2011 | WO |
2012075336 | Jun 2012 | WO |
2012145066 | Oct 2012 | WO |
2013067505 | May 2013 | WO |
Entry |
---|
Venezia, Paul , “NetDetector Captures Intrusions”, InfoWorld Issue 27, (“Venezia”), (Jul. 14, 2003). |
Vladimir Getov: “Security as a Service in Smart Clouds—Opportunities and Concerns”, Computer Software and Applications Conference (COMPSAC), 2012 IEEE 36th Annual, IEEE, Jul. 16, 2012 (Jul. 16, 2012). |
Wahid et al., Characterising the Evolution in Scanning Activity of Suspicious Hosts, Oct. 2009, Third International Conference on Network and System Security, pp. 344-350. |
Whyte, et al., “DNS-Based Detection of Scanning Works in an Enterprise Network”, Proceedings of the 12th Annual Network and Distributed System Security Symposium, (Feb. 2005), 15 pages. |
Williamson, Matthew M., “Throttling Viruses: Restricting Propagation to Defeat Malicious Mobile Code”, ACSAC Conference, Las Vegas, NV, USA, (Dec. 2002), pp. 1-9. |
Yuhei Kawakoya et al: “Memory behavior-based automatic malware unpacking in stealth debugging environment”, Malicious and Unwanted Software (Malware), 2010 5th International Conference on, IEEE, Piscataway, NJ, USA, Oct. 19, 2010, pp. 39-46, XP031833827, ISBN:978-1-4244-8-9353-1. |
Zhang et al., The Effects of Threading, Infection Time, and Multiple-Attacker Collaboration on Malware Propagation, Sep. 2009, IEEE 28th International Symposium on Reliable Distributed Systems, pp. 73-82. |
“Mining Specification of Malicious Behavior”—Jha et al, UCSB, Sep. 2007 https://www.cs.ucsb.edu/.about.chris/research/doc/esec07.sub.--mining.pdf-. |
“Network Security: NetDetector—Network Intrusion Forensic System (NIFS) Whitepaper”, (“NetDetector Whitepaper”), (2003). |
“When Virtual is Better Than Real”, IEEEXplore Digital Library, available at, http://ieeexplore.ieee.org/xpl/articleDetails.isp?reload=true&arnumbe- r=990073, (Dec. 7, 2013). |
Abdullah, et al., Visualizing Network Data for Intrusion Detection, 2005 IEEE Workshop on Information Assurance and Security, pp. 100-108. |
Adetoye, Adedayo , et al., “Network Intrusion Detection & Response System”, (“Adetoye”), (Sep. 2003). |
Apostolopoulos, George; hassapis, Constantinos; “V-eM: A cluster of Virtual Machines for Robust, Detailed, and High-Performance Network Emulation”, 14th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Sep. 11-14, 2006, pp. 117-126. |
Aura, Tuomas, “Scanning electronic documents for personally identifiable information”, Proceedings of the 5th ACM workshop on Privacy in electronic society. ACM, 2006. |
Baecher, “The Nepenthes Platform: An Efficient Approach to collect Malware”, Springer-verlag Berlin Heidelberg, (2006), pp. 165-184. |
Bayer, et al., “Dynamic Analysis of Malicious Code”, J Comput Virol, Springer-Verlag, France., (2006), pp. 67-77. |
Boubalos, Chris , “Extracting syslog data out of raw pcap dumps, seclists.org, Honeypots mailing list archives”, available at http://seclists.org/honeypots/2003/q2/319 (“Boubalos”), (Jun. 5, 2003). |
Chaudet, C. , et al., “Optimal Positioning of Active and Passive Monitoring Devices”, International Conference on Emerging Networking Experiments and Technologies, Proceedings of the 2005 ACM Conference on Emerging Network Experiment and Technology, CoNEXT '05, Toulousse, France, (Oct. 2005), pp. 71-82. |
Chen, P. M. and Noble, B. D., “When Virtual is Better Than Real, Department of Electrical Engineering and Computer Science”, University of Michigan (“Chen”) (2001). |
Cisco “Intrusion Prevention for the Cisco ASA 5500-x Series” Data Sheet (2012). |
Cohen, M.I. , “PyFlag—An advanced network forensic framework”, Digital investigation 5, Elsevier, (2008), pp. S112-S120. |
Costa, M. , et al., “Vigilante: End-to-End Containment of Internet Worms”, SOSP '05, Association for Computing Machinery, Inc., Brighton U.K., (Oct. 23-26, 2005). |
Didier Stevens, “Malicious PDF Documents Explained”, Security & Privacy, IEEE, IEEE Service Center, Los Alamitos, CA, US, vol. 9, No. 1, Jan. 1, 2011, pp. 80-82, XP011329453, ISSN: 1540-7993, DOI: 10.1109/MSP.2011.14. |
Distler, “Malware Analysis: An Introduction”, SANS Institute InfoSec Reading Room, SANS Institute, (2007). |
Dunlap, George W. , et al., “ReVirt: Enabling Intrusion Analysis through Virtual-Machine Logging and Replay”, Proceeding of the 5th Symposium on Operating Systems Design and Implementation, USENIX Association, (“Dunlap”), (Dec. 9, 2002). |
FireEye Malware Analysis & Exchange Network, Malware Protection System, FireEye Inc., 2010. |
FireEye Malware Analysis, Modern Malware Forensics, FireEye Inc., 2010. |
FireEye v.6.0 Security Target, pp. 1-35, Version 1.1, FireEye Inc., May 2011. |
Goel, et al., Reconstructing System State for Intrusion Analysis, Apr. 2008 SIGOPS Operating Systems Review, vol. 42 Issue 3, pp. 21-28. |
Gregg Keizer: “Microsoft's HoneyMonkeys Show Patching Windows Works”, Aug. 8, 2005, XP055143386, Retrieved from the Internet: URL:http://www.informationweek.com/microsofts-honeymonkeys-show-patching-windows-works/d/d-id/1035069? [retrieved on Jun. 1, 2016]. |
Heng Yin et al, Panorama: Capturing System-Wide Information Flow for Malware Detection and Analysis, Research Showcase @ CMU, Carnegie Mellon University, 2007. |
Hiroshi Shinotsuka, Malware Authors Using New Techniques to Evade Automated Threat Analysis Systems, Oct. 26, 2012, http://www.symantec.com/connect/blogs/, pp. 1-4. |
Idika et al., A-Survey-of-Malware-Detection-Techniques, Feb. 2, 2007, Department of Computer Science, Purdue University. |
Isohara, Takamasa, Keisuke Takemori, and Ayumu Kubota. “Kernel-based behavior analysis for android malware detection.” Computational intelligence and Security (CIS), 2011 Seventh International Conference on. IEEE, 2011. |
Kaeo, Merike , “Designing Network Security”, (“Kaeo”), (Nov. 2003). |
Kevin A Roundy et al: “Hybrid Analysis and Control of Malware”, Sep. 15, 2010, Recent Advances in Intrusion Detection, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 317-338, XP019150454 ISBN:978-3-642-15511-6. |
Khaled Salah et al: “Using Cloud Computing to Implement a Security Overlay Network”, Security & Privacy, IEEE, IEEE Service Center, Los Alamitos, CA, US, vol. 11, No. 1, Jan. 1, 2013 (Jan. 1, 2013). |
Kim, H. , et al., “Autograph: Toward Automated, Distributed Worm Signature Detection”, Proceedings of the 13th Usenix Security Symposium (Security 2004), San Diego, (Aug. 2004), pp. 271-286. |
King, Samuel T., et al., “Operating System Support for Virtual Machines”, (“King”), (2003). |
Kreibich, C. , et al., “Honeycomb-Creating Intrusion Detection Signatures Using Honeypots”, 2nd Workshop on Hot Topics in Networks (HotNets-11), Boston, USA, (2003). |
Kristoff, J. , “Botnets, Detection and Mitigation: DNS-Based Techniques”, NU Security Day, (2005), 23 pages. |
Lastline Labs, The Threat of Evasive Malware, Feb. 25, 2013, Lastline Labs, pp. 1-8. |
Li et al., A VMM-Based System Call Interposition Framework for Program Monitoring, Dec. 2010, IEEE 16th International Conference on Parallel and Distributed Systems, pp. 706-711. |
Lindorfer, Martina, Clemens Kolbitsch, and Paolo Milani Comparetti. “Detecting environment-sensitive malware.” Recent Advances in Intrusion Detection. Springer Berlin Heidelberg, 2011. |
Marchette, David J., “Computer Intrusion Detection and Network Monitoring: A Statistical Viewpoint”, (“Marchette”), (2001). |
Moore, D. , et al., “Internet Quarantine: Requirements for Containing Self-Propagating Code”, INFOCOM, vol. 3, (Mar. 30-Apr. 3, 2003), pp. 1901-1910. |
Morales, Jose A., et al., ““Analyzing and exploiting network behaviors of malware.””, Security and Privacy in Communication Networks. Springer Berlin Heidelberg, 2010. 20-34. |
Mori, Detecting Unknown Computer Viruses, 2004, Springer-Verlag Berlin Heidelberg. |
Natvig, Kurt , “SANDBOXII: Internet”, Virus Bulletin Conference, (“Natvig”), (Sep. 2002). |
NetBIOS Working Group. Protocol Standard for a NetBIOS Service on a TCP/UDP transport: Concepts and Methods. STD 19, RFC 1001, Mar. 1987. |
Newsome, J. , et al., “Dynamic Taint Analysis for Automatic Detection, Analysis, and Signature Generation of Exploits on Commodity Software”, In Proceedings of the 12th Annual Network and Distributed System Security, Symposium (NDSS '05), (Feb. 2005). |
Nojiri, D. , et al., “Cooperation Response Strategies for Large Scale Attack Mitigation”, DARPA Information Survivability Conference and Exposition, vol. 1, (Apr. 22-24, 2003), pp. 293-302. |
Oberheide et al., CloudAV.sub.--N-Version Antivirus in the Network Cloud, 17th USENIX Security Symposium USENIX Security '08 Jul. 28-Aug. 1, 2008 San Jose, CA. |
Reiner Sailer, Enriquillo Valdez, Trent Jaeger, Roonald Perez, Leendert van Doorn, John Linwood Griffin, Stefan Berger., sHype: Secure Hypervisor Appraoch to Trusted Virtualized Systems (Feb. 2, 2005) (“Sailer”). |
Silicon Defense, “Worm Containment in the Internal Network”, (Mar. 2003), pp. 1-25. |
Singh, S. , et al., “Automated Worm Fingerprinting”, Proceedings of the ACM/USENIX Symposium on Operating System Design and Implementation, San Francisco, California, (Dec. 2004). |
Thomas H. Ptacek, and Timothy N. Newsham , “Insertion, Evasion, and Denial of Service: Eluding Network Intrusion Detection”, Secure Networks, (“Ptacek”), (Jan. 1998). |
Number | Date | Country | |
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62527964 | Jun 2017 | US |