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 detecting and blocking attacks on electronics systems of a means of transportation. A protection module intercepts messages being transmitted on the buses of the means of transportation and saves the intercepted messages, and also for each intercepted message at least one ECU of the means of transportation which is the recipient of that message. The protection module detects computer attacks on the electronics systems by applying one or more rules, which can be received from a security server, to the saved data in the log. The rules may depend on one or more indicators of compromise that include malicious messages used in a computer attack and information on at least one ECU that is a recipient of the malicious messages. The described system further blocks the computer attacks by blocking, modifying, or changing communications within the communications bus of the vehicle.

Abstract: Disclosed is a system and method of updating active and passive agents in a network. The system includes a hardware processor configured to designate a unique identifier for each of a plurality of terminal node in a network of computing devices, broadcast the identifiers, collect criteria from the nodes, the criteria characterizing each node and a set of unique identifiers for other nodes in a same broadcast domain as the terminal node, generate a list of nodes that are active update agents and a list of nodes that are passive update agents based on the collected criteria, transmit one or more updates of a security application installed on the each terminal node to each terminal node that is an active update agent, and transmit from each terminal node that is an active update agent, the one or more updates to each terminal node that is a passive update agent.

Abstract: Systems and methods for detecting fraudulent activity in user transactions. An exemplary method includes, by a hardware processor, receiving user behavior data provided by an input device specifying a user interaction with graphical user interface (GUI) elements of a first application on a computing device for a transaction with a remote server, training a behavior classification algorithm using known behavior of the user, calculating an anomalous user behavior coefficient based on the user behavior data and the behavior classification algorithm, wherein the anomalous user behavior coefficient represents a likelihood that the user's interaction with the plurality of groups of elements of the graphical interface was fraudulent, detecting whether the user interaction is a software-imitated user interaction based on the anomalous user behavior coefficient, and responsive to detecting a software-imitated user interaction, blocking the transaction with the remote server.

Abstract: The present disclosure is directed towards systems and methods for detecting hidden behavior in browser extensions. In one aspect, a method is provided including launching a browser in a protected environment, performing one or more actions in the browser, tracking events occurring during the performing of the one or more actions, identifying extension events from the events that are initiated by a browser extension, analyzing the extension events for indications of change that correspond to behavior not previously declared by the browser extension, and determining that the browser extension is performing hidden behavior when indications of change are found.

Abstract: Disclosed are systems and method for trusted presentation of information on an untrusted user device. An exemplary system includes a secure portable device which can be connected to the untrusted user device and configured to: receive data from the untrusted user device; analyze the received data to identify therein information intended for display to the user via the untrusted user device; generate a video stream containing at least part of the information intended for display to the user; generate and insert into the video stream one or more protection elements that serve to authenticate the information being outputted in the video stream; and transmit the generated video stream to the user device.

Abstract: Disclosed herein are systems and methods of identifying malicious files using a learning model trained on a malicious file. In one aspect, an exemplary method comprises selecting, using a hardware processor, the malicious file from a plurality of malicious files that are known to be harmful, selecting, using the hardware processor, a plurality of safe files from a set of safe files that are known to be safe, generating, using the hardware processor, a learning model by training a neural network with the malicious file and the plurality of safe files, generating, using the hardware processor, rules for detection of malicious files from the learning model, determining, using the hardware processor, whether attributes of an unknown file fulfill the rules for detection of malicious files using the learning model and responsive to determining that the rules for detection are fulfilled, identifying, using the hardware processor, the unknown file as malicious.

Abstract: Systems and methods for countering a cyber attack on computing devices used by users gather data about services with which users are interacting, as well as data about devices used by users for such interactions. The collected data is analyzed to detect when a cyber-attack on the devices is occurring as a result of a data breach of personal data on users from at least one service. Actions are selected for countering the cyber-attack and are sent to the devices of all users of the corresponding cluster in the event that a match is found in the characteristics of the attack vector for at least one device of another user whose devices belong to the corresponding cluster.

Abstract: Disclosed are systems and methods for cloud detection, investigation and elimination of targeted attacks. In one exemplary aspect, the system comprises a computer protection module configured to: gather information on an object in a computer in a network; and save a security notification with the object in an object database in the network; and a module for protection against targeted attacks configured to: search for the object in a threat database in the network; add one or more tags to the object when the object is found in the threat database and adding a correspondence between a record in the object database and the threat database; and determine that a computer attack has occurred when the one or more tags correspond to signatures in a database of computer attacks.

Abstract: The present disclosure is directed to methods and systems for automated design of a system of hardware and software. In an exemplary embodiment, such a method comprises constructing, by a hardware processor, a model of use based on an architecture description of the system, constructing, by the hardware processor, threat model based on a threat description indicating known threats to the system, determining use of the system based on a comparison between the model of use and the threat model and selecting a configuration for realizing the system based on a result of the comparison.

Abstract: Disclosed herein are methods and systems of detecting malicious files. According to one aspect, a method comprises receiving one or more call logs from respectively one or more computers, each call log comprising function calls made from a file executing on a respective computer, combining the one or more call logs into a combined call log, searching the combined call log to find a match for one or more behavioral rules stored in a threat database, determining, when the behavioral rules are found in the call log, a verdict about the file being investigated and transmitting information regarding the verdict to the one or more computers.

Abstract: Techniques are provided for downloading of filtering rules from a remote server onto a mobile device. A priority list is determined from lists of filtering rules, the priority list having a highest indicator of frequency of actuation of the filtering rules from the lists. The filtering rules are designated for use by a first application on the mobile device. The priority list is transmitted to the mobile device with the aid of a second application, the second application on the mobile device being a provider of the filtering rules for the first application. Each of the remaining lists of filtering rules are divided into parts. Groups of filtering rules are generated based on frequency of actuation within each of the remaining lists of filtering rules, each group having not more than one part of each remaining list of filtering rules.

Abstract: Disclosed are systems and methods for emulating execution of a file based on emulation time. In one aspect, an exemplary method comprises, generating an image of a file, emulating an execution of instructions from the image for a predetermined emulation time, the emulation including: when an emulation of an execution of instruction from an image of another file is needed, generating an image of the another file, detecting known set of instructions in portions read from the image, inserting a break point into a position in the generated image corresponding to a start of the detected set of instructions, emulating execution of the another file by emulating execution of instructions from the generated image, and adding corresponding records to an emulation log, and reading a next portion from the image of the another file and repeating the emulation until the predetermined emulation time has elapsed.

Abstract: The present disclosure provides a system for managing computer resources for detection of malicious files based on machine learning model. In one aspect, the system may comprise: a hardware processor configured to: form at least one behavior pattern on the basis of commands and parameters, calculate the convolution of the formed behavior pattern, calculate the degree of harmfulness the convolution and a model for detection of malicious files, manage the computing resources used to ensure the security of that computing device, based on the degree of harmfulness, wherein the degree of harmfulness is within a predetermined range of values and if the obtained degree of harmfulness of applications exceeds the predetermined threshold value, send a request to allocate additional resources of the computing device, otherwise send a request to free up previously allocated resources of the computing device.

Abstract: Disclosed herein are systems and methods of creating antivirus records. An exemplary method comprises: analyzing, by a protector against targeted attacks, a log of API function calls of a file for presence of malicious behavior using one or more behavioral rules; determining that the file is malicious when a behavioral rule corresponding to records of a log of API function calls is identified; extracting one or more records of API function calls associated with the identified behavioral rule; determining whether at least one extracted record of the API function calls can be registered by a protector of a computing device; and when the at least one extracted record can be registered by the protector of the computing device, creating an antivirus record for the protector of the computing device, wherein the created antivirus record includes at least the extracted records of the API function calls.

Abstract: Disclosed are systems and methods for machine learning of a model for detecting malicious files. The described system samples files from a database of files and trains a detection model for detecting malicious files on the basis of an analysis of the sampled files. The described system forms behavior logs based on executable commands intercepted during execution of the sampled files, and generates behavior patterns based on the behavior log. The described system determines a convolution function based on the behavior patterns, and trains a detection model for detecting malicious files by calculating parameters of the detection model using the convolution function on the behavior patterns. The trained detection model may be used to detect malicious files by utilizing the detection model on a system behavior log generated during execution of suspicious files.

Abstract: Disclosed are systems and methods for preserving of data saved on a data storage device. An assessment is made as to the degradation of the data storage device, during which a determination is made of the rate of degradation and the probability of failure of the data storage device. When the probability is greater than a given threshold, the damaged sectors of the data storage device are identified by scanning of the surface of the data storage device. A worth grade (i.e., the value of the saved data to the user) is determined at least for data in sectors close to the damaged sectors on the basis of an analysis of at least the meta-data of the data. A decision is made as to the possible loss of data, and a backup copy is created based on the worth grade of the data and the rate of degradation of the data storage device.

Abstract: Disclosed are systems and methods for detection of malicious intermediate language files. In one exemplary aspect, the system comprises a database comprising hashes of known malicious files, a resource allocation module configured to select a set of resources from a file being analyzed, a hash calculation module, coupled to the resource allocation module, configured to calculate a perceptive hash of the set of resources; and an analysis module, coupled to the other modules, configured to identify a degree of similarly between the set of resources and a set of resources from known malicious files by comparing the perceptive hash with perceptive hashes of the set of resources from known malicious files, determine a harmfulness of the file being analyzed based on the degree of similarity and remove or quarantine the file being analyzed when the harmfulness exceeds a predetermined threshold.

Abstract: Disclosed are methods and systems for detecting malicious codes in the address space of processes. The described method detects a launching of a process from an executable file executing on a computer, detects access to a address within a memory area in an address space of the trusted process, wherein the memory area is a memory area that lies outside the boundaries of the trusted executable image representing the executable file and is an executable memory area, analyzes memory areas within a vicinity of the address space to determine whether another executable image is located in the memory areas, analyzing the another executable image to determine whether the other executable image contains malicious code, concluding malicious code is contained in the address space of the trusted process when the another executable image contains malicious code and performing one of removing, halting or quaranting the malicious code from the address space.

Abstract: Disclosed are a system, a method, and computer readable storage medium having instructions for filtering network traffic to protect a server from a distributed denial-of-service (DDoS) attack. The described technique includes intercepting data from a network node to the computing device responsive to detecting a computing device is subject to a DDoS attack. The technique further includes determining one or more data transmission parameters based on the intercepted data, assigning a danger rating to the network node, and changing the danger rating of the network node based on application of a filter and on the data transmission parameters. The described technique limits a transmittal of data from the network node to the computing device if the resultant danger rating of the network node exceeds a threshold value.