Disclosed are systems, methods and computer program products for detection, classification and reporting of malicious software. A method comprises loading software code into a computer system memory and emulating the software code. The software code and its activity log are then analyzed for presence of a malware. If a malware is detected, an execution flow graph is created from the activity log. The execution flow graph is then parsed using heuristic analysis to identify one or more malicious behavior patterns therein. Then, similarity indexes between the identified malicious behavior patterns and one or more malicious behavior patterns associated with known classes of malware are computed. The emulated software code is then classified into one or more classes of malware based on the computed similarity indexes. Finally, a comprehensive malware report of the emulated software code is generated based on the execution flow graph and malware classification information.

Systems and methods for malware classification

Exemplary systems and methods for detecting a communication channel of a bot In exemplary embodiments, presence of a communication channel between a first network device and a second network device is detected Data from the communication channel is scanned and used to determine if a suspected bot communication exists If a bot communication is detected, then a recovery process may be initiated

System and method for bot detection

Described are techniques for capturing and analyzing snapshots of virtual machines. One or more computers may automatically obtain snapshots of virtual machines as they are executing to form a pool of virtual machine snapshots. The virtual machine snapshots are then read to obtain a set of features properties of the virtual machine snapshots, including information about a running guest operating system, software installed on the virtual machine, metadata about the virtual machine itself, and others. The features or properties are analyzed, in one embodiment using a machine learning algorithm, to automatically compute and store information about the virtual machines.

Virtual machine snapshotting and analysis

Provided are an apparatus and method for detecting a Dynamic Link Library (DLL) inserted by a malicious code. The method includes collecting first DLL information from an image file of a process before the process is executed; collecting second DLL information loaded into a memory as the process is executed; comparing the first DLL information with the second DLL information to extract information on an explicit DLL; and determining whether the explicit DLL is a DLL inserted by a malicious code or not.

Apparatus and method for detecting DLL inserted by malicious code

Method and system for automated incident response

An automated threat analysis system comprising a core in an isolated environment, the core associated with an input interface and an output interface. The core comprises one or more core components and an operating system having at least one library hooked to at least one of the one or more core components. In use, a threat (eg. malicious software) is passed into the core via the input interface and the threat is executed in the core using the operating system. Report data is generated by the one or more core components which monitors the functions/processes occurring in the system as a result of the threat, and the report data is passed out of the core via the output interface according to a predefined format so as to isolate any output from or escape of the threat.

Automated threat analysis

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