Abstract: Examples of the present disclosure describe systems and methods for malicious software detection based on API trust. In an example, a set of software instructions executed by a computing device may call an API. A hook may be generated on the API, such that a threat processor may receive an indication when the API is called. Accordingly, the threat processor may generate a trust metric based on the execution of the set of software instructions, which may be used to determine whether the set of software instructions poses a potential threat. For example, one or more call stack frames may be evaluated to determine whether a return address is preceded by a call instruction, whether the return address is associated with a set of software instructions or memory associated with a set of software instructions, and/or whether the set of software instructions satisfies a variety of security criteria.

Abstract: Examples of the present disclosure describe systems and methods for discrete processor feature behavior collection and analysis. In aspects, a monitoring utility may initialize a set of debugging and/or performance monitoring feature sets for a microprocessor. When the microprocessor receives from software content a set of instructions that involves the loading of a set of modules or code segments, the set of modules or code segments may be evaluated by the monitoring utility. The monitoring utility may generate a process trace of the loaded set of modules or code segments. Based on the process trace output, various execution paths may be reconstructed in real-time. The system and/or API calls made by the microprocessor may then be compared to the process trace output to quickly observe the interaction between the software content and the operating system of the microprocessor.

Abstract: Examples of the present disclosure describe systems and methods for selective export address table filtering. In aspects, the relative virtual address (RVA) of exported function names may be modified to point to a protected memory location. An exception handler may be registered to process exceptions relating to access violations of the protected memory location. If an exception is detected that indicates an attempt to access the protected memory location, the instruction pointer of the exception may be compared to an allowed range of memory addresses. If the instruction pointer address is outside the boundaries, remedial action may occur.

Abstract: Examples of the present disclosure describe systems and methods for discrete processor feature behavior collection and analysis. In aspects, a monitoring utility may initialize a set of debugging and/or performance monitoring feature sets for a microprocessor. When the microprocessor receives from software content a set of instructions that involves the loading of a set of modules or code segments, the set of modules or code segments may be evaluated by the monitoring utility. The monitoring utility may generate a process trace of the loaded set of modules or code segments. Based on the process trace output, various execution paths may be reconstructed in real-time. The system and/or API calls made by the microprocessor may then be compared to the process trace output to quickly observe the interaction between the software content and the operating system of the microprocessor.

Abstract: Examples of the present disclosure describe systems and methods for a behavioral threat detection engine. In examples, the behavioral threat detection engine manages execution of one or more virtual machines, wherein each virtual machine processes a rule in relation to a context. The behavioral threat detection engine uses any of a variety of techniques to identify when events occur. Accordingly, the behavioral threat detection engine provides event indications, in the form of event packets, to one or more virtual machines, such that corresponding rules are able to process the events accordingly. Eventually, a rule may make a determination as to the presence or absence of a behavior. As a result, execution of the associated virtual machine may be halted, thereby indicating to the behavioral threat detection engine that a determination has been made. Thus a behavioral threat detection engine employs a behavior-based approach to detecting malicious or potentially malicious behaviors.

Abstract: Examples of the present disclosure describe systems and methods for a behavioral threat detection engine. In examples, the behavioral threat detection engine manages execution of one or more virtual machines, wherein each virtual machine processes a rule in relation to a context. The behavioral threat detection engine uses any of a variety of techniques to identify when events occur. Accordingly, the behavioral threat detection engine provides event indications, in the form of event packets, to one or more virtual machines, such that corresponding rules are able to process the events accordingly. Eventually, a rule may make a determination as to the presence or absence of a behavior. As a result, execution of the associated virtual machine may be halted, thereby indicating to the behavioral threat detection engine that a determination has been made. Thus a behavioral threat detection engine employs a behavior-based approach to detecting malicious or potentially malicious behaviors.

Abstract: Examples of the present disclosure describe systems and methods for a behavioral threat detection virtual machine. In examples, the virtual machine executes a rule comprising rule instructions. A rule may comprise one or more wait rule instructions that causes the virtual machine to pause execution. As events are added to an event queue for the rule virtual machine, the behavioral threat detection virtual machine evaluates such events in order to identify a positive or, in some instances, a negative match. When a matching event is identified, rule execution resumes. Eventually, a determination is made as a result of processing events and wait packets, thereby indicating the presence or absence of a malicious or potentially malicious behavior, among other examples. Thus, among other things, the behavioral threat detection virtual machine maintains a state associated with rule execution and processes events to identify behaviors accordingly.

Abstract: Examples of the present disclosure describe systems and methods for behavioral threat detection definition. In an example, a behavior rule comprising a set of rule instructions is used to define one or more events indicative of a behavior. For example, a set of events from which one event must be matched may be defined or a set of events from which all events must be matched may be defined. In some examples, events are matched based on an event name or type, or may be matched based on one or more parameters. Exact and/or inexact matching may be used. The set of rule instructions ultimately specifies one or more halt instructions, thereby indicating that a determination as to the presence of the behavior has been made. Example determinations include, but are not limited to, a match determination, a non-match determination, or an indication that additional monitoring should be performed.

Abstract: Examples of the present disclosure describe systems and methods for selective export address table filtering. In aspects, the relative virtual address (RVA) of exported function names may be modified to point to a protected memory location. An exception handler may be registered to process exceptions relating to access violations of the protected memory location. If an exception is detected that indicates an attempt to access the protected memory location, the instruction pointer of the exception may be compared to an allowed range of memory addresses. If the instruction pointer address is outside the boundaries, remedial action may occur.

Abstract: Examples of the present disclosure describe systems and methods for providing advanced file modification heuristics. In aspects, software content is selected for monitoring. The monitoring comprises determining when the software content performs file accesses that are followed by read and/or write operations. The read/write operations are analyzed in real-time to determine whether the software content is modifying file content. If the monitoring indicates the software content is modifying accessed files, mathematical calculations are applied to the read-write operations to determine the nature of the modifications. Based on the determined nature of the file modifications, the actions of the software content may be categorized and halted prior to completion; thereby, mitigating malicious cyberattacks and/or unauthorized accesses.

Abstract: Examples of the present disclosure describe systems and methods for behavioral threat detection definition. In an example, a behavior rule comprising a set of rule instructions is used to define one or more events indicative of a behavior. For example, a set of events from which one event must be matched may be defined or a set of events from which all events must be matched may be defined. In some examples, events are matched based on an event name or type, or may be matched based on one or more parameters. Exact and/or inexact matching may be used. The set of rule instructions ultimately specifies one or more halt instructions, thereby indicating that a determination as to the presence of the behavior has been made. Example determinations include, but are not limited to, a match determination, a non-match determination, or an indication that additional monitoring should be performed.

Abstract: Examples of the present disclosure describe systems and methods for a behavioral threat detection virtual machine. In examples, the virtual machine executes a rule comprising rule instructions. A rule may comprise one or more wait rule instructions that causes the virtual machine to pause execution. As events are added to an event queue for the rule virtual machine, the behavioral threat detection virtual machine evaluates such events in order to identify a positive or, in some instances, a negative match. When a matching event is identified, rule execution resumes. Eventually, a determination is made as a result of processing events and wait packets, thereby indicating the presence or absence of a malicious or potentially malicious behavior, among other examples. Thus, among other things, the behavioral threat detection virtual machine maintains a state associated with rule execution and processes events to identify behaviors accordingly.

Abstract: Examples of the present disclosure describe systems and methods for malicious software detection based on API trust. In an example, a set of software instructions executed by a computing device may call an API. A hook may be generated on the API, such that a threat processor may receive an indication when the API is called. Accordingly, the threat processor may generate a trust metric based on the execution of the set of software instructions, which may be used to determine whether the set of software instructions poses a potential threat. For example, one or more call stack frames may be evaluated to determine whether a return address is preceded by a call instruction, whether the return address is associated with a set of software instructions or memory associated with a set of software instructions, and/or whether the set of software instructions satisfies a variety of security criteria.

Abstract: Examples of the present disclosure describe systems and methods for selective export address table filtering. In aspects, when an executable program is loaded in an operating system's execution environment, an address table filtering (ATF) module is loaded into the address space of a target process associated with the executable program. The ATF module may iterate a list of system library files to identify exported function names. The relative virtual address (RVA) of the exported function names may be modified to point to a protected memory location. An exception handler may be registered to process exceptions relating to access violations of the protected memory location. If the exception handler determines that an access violation caused the detected exception, the instruction pointer of the exception may be compared to the expected system library addresses boundaries. If the instruction pointer address is outside the boundaries, remedial action may occur.

Abstract: Examples of the present disclosure describe systems and methods for providing advanced file modification heuristics. In aspects, software content is selected for monitoring. The monitoring comprises determining when the software content performs file accesses that are followed by read and/or write operations. The read/write operations are analyzed in real-time to determine whether the software content is modifying file content. If the monitoring indicates the software content is modifying accessed files, mathematical calculations are applied to the read-write operations to determine the nature of the modifications. Based on the determined nature of the file modifications, the actions of the software content may be categorized and halted prior to completion; thereby, mitigating malicious cyberattacks and/or unauthorized accesses.

Abstract: Examples of the present disclosure describe systems and methods for a behavioral threat detection engine. In examples, the behavioral threat detection engine manages execution of one or more virtual machines, wherein each virtual machine processes a rule in relation to a context. The behavioral threat detection engine uses any of a variety of techniques to identify when events occur. Accordingly, the behavioral threat detection engine provides event indications, in the form of event packets, to one or more virtual machines, such that corresponding rules are able to process the events accordingly. Eventually, a rule may make a determination as to the presence or absence of a behavior. As a result, execution of the associated virtual machine may be halted, thereby indicating to the behavioral threat detection engine that a determination has been made. Thus a behavioral threat detection engine employs a behavior-based approach to detecting malicious or potentially malicious behaviors.

Abstract: Examples of the present disclosure describe systems and methods for malicious software detection based on API trust. In an example, a set of software instructions executed by a computing device may call an API. A hook may be generated on the API, such that a threat processor may receive an indication when the API is called. Accordingly, the threat processor may generate a trust metric based on the execution of the set of software instructions, which may be used to determine whether the set of software instructions poses a potential threat. For example, one or more call stack frames may be evaluated to determine whether a return address is preceded by a call instruction, whether the return address is associated with a set of software instructions or memory associated with a set of software instructions, and/or whether the set of software instructions satisfies a variety of security criteria.

Abstract: Examples of the present disclosure describe systems and methods for a behavioral threat detection engine. In examples, the behavioral threat detection engine manages execution of one or more virtual machines, wherein each virtual machine processes a rule in relation to a context. The behavioral threat detection engine uses any of a variety of techniques to identify when events occur. Accordingly, the behavioral threat detection engine provides event indications, in the form of event packets, to one or more virtual machines, such that corresponding rules are able to process the events accordingly. Eventually, a rule may make a determination as to the presence or absence of a behavior. As a result, execution of the associated virtual machine may be halted, thereby indicating to the behavioral threat detection engine that a determination has been made. Thus a behavioral threat detection engine employs a behavior-based approach to detecting malicious or potentially malicious behaviors.

Abstract: Examples of the present disclosure describe systems and methods for monitoring the security privileges of a process. In aspects, when a process is created, the corresponding process security token and privilege information is detected and recorded. At subsequent “checkpoints,” the security token is evaluated to determine whether the security token has been replaced, or whether new or unexpected privileges have been granted to the created process. When a modification to the security token is determined, a warning or indication of the modification is generated and the process may be terminated to prevent the use of the modified security token.

Abstract: Examples of the present disclosure describe systems and methods for monitoring the security privileges of a process. In aspects, when a process is created, the corresponding process security token and privilege information is detected and recorded. At subsequent “checkpoints,” the security token is evaluated to determine whether the security token has been replaced, or whether new or unexpected privileges have been granted to the created process. When a modification to the security token is determined, a warning or indication of the modification is generated and the process may be terminated to prevent the use of the modified security token.