Abstract: A method for detecting a vulnerability in an operating system based on process and thread data, includes the steps of: detecting one or more launches of one or more threads associated with one or more processes in an operating system (OS); generating a set of privileges based on the detected one or more launches; analyzing the generated set of privileges to identify illegitimate changes in privileges; detecting a vulnerability in the OS using one or more rules for detecting a vulnerability based on the analyzed set of privileges; and isolating a file that exploited the detected vulnerability, in response to detecting the vulnerability.

Abstract: Disclosed herein are systems and methods for forming a log during an execution of a file with vulnerabilities. In one aspect, an exemplary method comprises, discovering an activation of a trigger during an execution of a thread of a process created upon opening the file, wherein the trigger describes conditions accompanying an event which relates to an attempt to exploit a vulnerability of the file, analyzing a stack of the process created upon opening the file, and discovering a chain of function calls preceding the event in a form of a sequence of call and return addresses, analyzing the discovered chain of function calls for fulfillment of conditions of the trigger which relate to the attempt to exploit the vulnerability, and when the conditions of the trigger are fulfilled, saving information about the chain of function calls in a log.

Abstract: A system and method is provided for detecting anomalous events based on a dump of an address space of a software process in a memory of a computing device. An exemplary method includes detecting at least one event occurring in an operating system of the computing device during an execution of the software process, determining a context of the detected event, wherein the context comprises a dump of an address space of the software process containing code that was being executed at the moment of occurrence of the detected event, selecting a set of features of the dump for use in determining whether or not the event is anomalous, transforming the selected set of features of the dump into a convolution, determining a popularity of the convolution by polling a database, and determining that the detected event is an anomalous event if the determined popularity is below a threshold value.

Abstract: A system and method is provided for providing a set of convolutions to a computing device for detecting anomalous events occurring in an operating system of the computing device. An exemplary method includes launching an agent in an operating system of a client device, registering, by the agent, events occurring in the operating system, for each registered event, determining a context of the event, wherein the context comprises a call stack at a moment of occurrence of the event, selecting a set of features based on the call stack of the event, generating a convolution based on the selected set of features of the event and the context of the event, and adding the generated convolution to a set of convolutions of events occurring on client devices, and providing, to a client device from which a request is received, the set of convolutions of events occurring on client devices.

Abstract: Disclosed are systems and methods for adapting a pattern of dangerous behavior of programs. A teaching module may load into an activity monitor the pattern and establish a first usage mode for it, during which the activity monitor detects threats that correspond to that pattern, but does not perform actions for their removal. Later, in the course of a teaching period, the activity monitor detects threats based on the detection of events from the mentioned pattern. If the events have occurred as a result of user actions, and the events have a recurring nature or are regular in nature, the teaching module adds parameters to the pattern which exclude from subsequent detection those events or similar events. Upon expiration of the teaching period, the teaching module converts the pattern of dangerous behavior of programs to the second usage mode, during which threats are detected using the modified pattern and removed.

Abstract: Disclosed are systems and methods for adapting a pattern of dangerous behavior of programs. A teaching module may load into an activity monitor the pattern and establish a first usage mode for it, during which the activity monitor detects threats that correspond to that pattern, but does not perform actions for their removal. Later, in the course of a teaching period, the activity monitor detects threats based on the detection of events from the mentioned pattern. If the events have occurred as a result of user actions, and the events have a recurring nature or are regular in nature, the teaching module adds parameters to the pattern which exclude from subsequent detection those events or similar events. Upon expiration of the teaching period, the teaching module converts the pattern of dangerous behavior of programs to the second usage mode, during which threats are detected using the modified pattern and removed.

Abstract: Disclosed herein are systems and methods for forming a log during an execution of a file with vulnerabilities. In one aspect, an exemplary method comprises, discovering an activation of a trigger during an execution of a thread of a process created upon opening the file, wherein the trigger describes conditions accompanying an event which relates to an attempt to exploit a vulnerability of the file, analyzing a stack of the process created upon opening the file, and discovering a chain of function calls preceding the event in a form of a sequence of call and return addresses, analyzing the discovered chain of function calls for fulfillment of conditions of the trigger which relate to the attempt to exploit the vulnerability, and when the conditions of the trigger are fulfilled, saving information about the chain of function calls in a log.

Abstract: A system and method is provided for detecting anomalous events based on a dump of an address space of a software process in a memory of a computing device. An exemplary method includes detecting at least one event occurring in an operating system of the computing device during an execution of the software process, determining a context of the detected event, wherein the context comprises a dump of an address space of the software process containing code that was being executed at the moment of occurrence of the detected event, selecting a set of features of the dump for use in determining whether or not the event is anomalous, transforming the selected set of features of the dump into a convolution, determining a popularity of the convolution by polling a database, and determining that the detected event is an anomalous event if the determined popularity is below a threshold value.

Abstract: Disclosed are systems and methods for controlling opening of computer files by vulnerable applications. An example method includes: responsive to detecting creation by a source software application of a computer file on the user computer, determining a file access policy associated with the computer file based on one or more parameters of the computer file; responsive to detecting a request from a consumer software application to open the computer file, determining an application launching policy associated with the consumer software application based on one or more vulnerabilities identified for the consumer software application; determining a file opening policy associated with the computer file and the consumer software application based on the file access policy, the application launching policy, and respective priorities amongst the policies; and controlling opening of the computer file by the consumer software application according to the determined file opening policy.

Abstract: A system and method is provided for providing a set of convolutions to a computing device for detecting anomalous events occurring in an operating system of the computing device. An exemplary method includes launching an agent in an operating system of a client device, registering, by the agent, events occurring in the operating system, for each registered event, determining a context of the event, wherein the context comprises a call stack at a moment of occurrence of the event, selecting a set of features based on the call stack of the event, generating a convolution based on the selected set of features of the event and the context of the event, and adding the generated convolution to a set of convolutions of events occurring on client devices, and providing, to a client device from which a request is received, the set of convolutions of events occurring on client devices.

Abstract: A system and method is provided for detecting anomalous events occurring in an operating system of a computing device. An exemplary method includes detecting an event that occurs in the operating system of the computing device during execution of a software process. Moreover, the method includes determining a context of the detected event and forming a convolution of the detected event based on selected features of the determined context of the detected event. Further, the method includes determining a popularity of the formed convolution by polling a database containing data relating to a frequency of detected events occurring in client devices in a network, where the detected events of the client devices correspond to the detected event in the computing device. If the determined popularity is below a threshold value, the method determines that the detected event is an anomalous event.

Abstract: A system and method is provided for detecting anomalous events occurring in an operating system of a computing device. An exemplary method includes detecting an event that occurs in the operating system of the computing device during execution of a software process. Moreover, the method includes determining a context of the detected event and forming a convolution of the detected event based on selected features of the determined context of the detected event. Further, the method includes determining a popularity of the formed convolution by polling a database containing data relating to a frequency of detected events occurring in client devices in a network, where the detected events of the client devices correspond to the detected event in the computing device. If the determined popularity is below a threshold value, the method determines that the detected event is an anomalous event.

Abstract: A system and method is provided for detecting anomalous events occurring in an operating system of a computing device. An exemplary method includes detecting an event that occurs in the operating system of the computing device during execution of a software process. Moreover, the method includes determining a context of the detected event and forming a convolution of the detected event based on selected features of the determined context of the detected event. Further, the method includes determining a popularity of the formed convolution by polling a database containing data relating to a frequency of detected events occurring in client devices in a network, where the detected events of the client devices correspond to the detected event in the computing device. If the determined popularity is below a threshold value, the method determines that the detected event is an anomalous event.

Abstract: Disclosed are system and method for detecting malicious code in files. One exemplary method comprises: intercepting, by a processor, one or more application program interface (API) calls during an execution of a process launched from a file of a computing device; determining and detecting, by the processor, a presence of an exit condition of the process; in response to detecting the exit condition, identifying one or more signatures of a first type and transferring one or more saved memory dumps of the computing device to an emulator for execution; and determining and identifying a malicious code in the file in response to detecting one or more signatures of a second type based at least upon execution results of the transferred memory dumps of the computing device.

Abstract: Disclosed are systems and methods for adapting a pattern of dangerous behavior of programs. A teaching module may load into an activity monitor the pattern and establish a first usage mode for it, during which the activity monitor detects threats that correspond to that pattern, but does not perform actions for their removal. Later, in the course of a teaching period, the activity monitor detects threats based on the detection of events from the mentioned pattern. If the events have occurred as a result of user actions, and the events have a recurring nature or are regular in nature, the teaching module adds parameters to the pattern which exclude from subsequent detection those events or similar events. Upon expiration of the teaching period, the teaching module converts the pattern of dangerous behavior of programs to the second usage mode, during which threats are detected using the modified pattern and removed.

Abstract: Disclosed are systems and methods for adapting a pattern of dangerous behavior of programs. A teaching module may load into an activity monitor the pattern and establish a first usage mode for it, during which the activity monitor detects threats that correspond to that pattern, but does not perform actions for their removal. Later, in the course of a teaching period, the activity monitor detects threats based on the detection of events from the mentioned pattern. If the events have occurred as a result of user actions, and the events have a recurring nature or are regular in nature, the teaching module adds parameters to the pattern which exclude from subsequent detection those events or similar events. Upon expiration of the teaching period, the teaching module converts the pattern of dangerous behavior of programs to the second usage mode, during which threats are detected using the modified pattern and removed.

Abstract: Disclosed are system and method for detecting malicious code in address space of a process. An exemplary method comprises: detecting a first process executed on the computer in association with an application; intercepting at least one function call made by the first process to a second process; determining one or more attributes associated with the at least one function call; determining whether to perform malware analysis of code associated with the at least one function call in an address space associated with the second process based on application of one or more rules to the one or more attributes; and upon determining to perform malware analysis of the code, determining whether the code in the address space is malicious.

Abstract: A system and method is provided for detecting anomalous events occurring in an operating system of a computing device. An exemplary method includes detecting an event that occurs in the operating system of the computing device during execution of a software process. Moreover, the method includes determining a context of the detected event and forming a convolution of the detected event based on selected features of the determined context of the detected event. Further, the method includes determining a popularity of the formed convolution by polling a database containing data relating to a frequency of detected events occurring in client devices in a network, where the detected events of the client devices correspond to the detected event in the computing device.

Abstract: A system and method is provided for detecting anomalous events occurring in an operating system of a computing device. An exemplary method includes detecting an event that occurs in the operating system of the computing device during execution of a software process. Moreover, the method includes determining a context of the detected event and forming a convolution of the detected event based on selected features of the determined context of the detected event. Further, the method includes determining a popularity of the formed convolution by polling a database containing data relating to a frequency of detected events occurring in client devices in a network, where the detected events of the client devices correspond to the detected event in the computing device.

Abstract: A system and method is provided for detecting anomalous events occurring in an operating system of a computing device. An exemplary method includes detecting an event that occurs in the operating system of the computing device during execution of a software process. Moreover, the method includes determining a context of the detected event and forming a convolution of the detected event based on selected features of the determined context of the detected event. Further, the method includes determining a popularity of the formed convolution by polling a database containing data relating to a frequency of detected events occurring in client devices in a network, where the detected events of the client devices correspond to the detected event in the computing device.