Aspects of the disclosure are directed to a system for classifying software as malicious or benign based on predicting the effect the software has on the platform before the software is actually deployed. A system as described herein can operate in close to real-time to receive, isolate, and classify software as benign or malicious. Aspects of the disclosure provide for accurate classification of malicious programs or scripts even if ostensibly the program appears benign, and vice versa, based on the effect predicted by a machine learning model trained as described herein. The system can also be implemented to isolate and verify incoming scripts or software to the platform, to provide a predicted classification while not substantially impacting processing pipelines involving platform resources or the user experience with the platform in general.

In some examples, a system receives first measurements of data items used by a build server in building an executable program, the data items copied from a data repository to a storage partition that is separate from the data repository, and the storage partition to store the data items relating to building the executable program by the build server. The system determines, based on the first measurements and according to a policy specified for the storage partition, whether a corruption of the data items used by the build server in building the executable program has occurred.

Provided are a cyber threat information processing apparatus, a method thereof, and a storage medium storing a cyber threat information processing program. It is possible to provide a cybersecurity threat information processing method including disassembling an input executable file to obtain disassembled code, and reconstructing the disassembled code to obtain reconstructed disassembled code, into a hash function, and converting the hash function into N-gram data (N being a natural number), and performing ensemble machine learning on block-unit code of the converted N-gram data to profile the block-unit code by an identifier of an attack technique performed by the block-unit code and an identifier of an attacker generating the block-unit code. It is possible to detect and address a variant of malware, and identify malware, an attack technique, an attacker, and an attack prediction method within a significantly short time even for a variant of malware.

Disclosed are systems, methods, and non-transitory computer-readable storage media for malware detection and content item recovery. For example, a content management system can receive information describing changes made to content items stored on a user device. The content management system can analyze the information to determine if the described changes are related to malicious software on the user device. When the changes are related to malicious software, the content management system can determine which content items are effected by the malicious software and/or determine when the malicious software first started making changes to the user device. The content management system can recover effected content items associated with the user device by replacing the effected versions of the content items with versions of the content items that existed immediately before the malicious software started making changes to the user device.

This disclosure is related to methods and apparatus used to for preventing malicious content from reaching a destination via a dynamic analysis engine may operate in real-time when packetized data is received. Data packets sent from a source computer may be received and be forwarded to an analysis computer that may monitor actions performed by executable program code included within the set of data packets when making determinations regarding whether the data packet set should be classified as malware. In certain instances all but a last data packet of the data packet set may also be sent to the destination computer while the analysis computer executes and monitors the program code included in the data packet set. In instances when the analysis computer identifies that the data packet set does include malware, the malware may be blocked from reaching the destination computer by not sending the last data packet to the destination computer.

A method for a cyber threat defense system is provided. The method comprises receiving a first abnormal behavior pattern where the first abnormal behavior pattern represents behavior on a first network deviating from a normal benign behavior of that network; and receiving a second abnormal behavior pattern where the second abnormal behavior pattern representing either behavior on the first network or on a second network deviating from a normal benign behavior of that network. The method further comprises comparing the first and second abnormal behavior patterns to determine a similarity score between the first and second abnormal behavior patterns and determining, based on the comparison, that the first abnormal behavior pattern likely corresponds to malicious behavior when the similarity score is above a threshold. A corresponding non-transitory computer readable medium is also provided.

A file is stored in a public cloud storage. A serverless computing platform receives an event notification that the file has been stored and, in response, creates an instance of an ephemeral environment wherein a security module is executed. The security module creates a memory-mapped space with memory locations that are mapped to the entire content of the file but does not allocate memory for all of the memory locations. Instead, the security module retrieves sections of the file from the public cloud storage as these sections are accessed in their designated memory locations in accordance with the memory mapping, allocates memory for the retrieved sections, stores the retrieved sections in their designated memory locations, and scans the retrieved sections in their designated memory locations for malicious code. The security module continues scanning the file in sections until relevant sections of the file have been scanned.

A code repository stores source code. An insider threat detection system stores instructions for detecting code defects and criteria indicating predetermined types of code defects that, when present, are associated with intentional obfuscation of one or more functions of the source code. The insider threat detection system receives an entry of source code and detects, using the model, a set of code defects in the entry of source code. A defect type is determined for each code defect, thereby determining a set of defect types included in the entry of source code. If it is determined that each of the predetermined types of code defects indicated by the criteria is included in the determined set of defect types, the entry of source code is determined to include an insider threat.

A system, method and computer program product for open innovation including an asset valuation device receiving asset information about tangible or non-tangible assets, and generating a valuation signal, based on the asset information; a self-executing code device receiving the valuation signal, and generating a self-executing code signal, based on the valuation signal; an air router device having both a low band radio channel, and an internet router channel for redundant internet communications, and a malicious code removal device for scrubbing malicious code from data received, receiving the valuation signal, and generating a node voting request signal, based on the valuation signal; and a mesh network having a plurality of node devices receiving the node voting request signal, and generating vote confirmation signals, based on the node voting request signal. Computing devices are connected to the node devices to perform problem solving, smart contract processing, and/or cryptocurrency mining.

An anti-malware device 50 includes: a risk information storage unit 51 in which risk information 510 is stored, in which there are associated a value indicating an attribution of an information processing device 60 for executing software 600, a value indicating an attribution of the software 600, and a value that indicates the degree of risk when the software 600 is executed; a subject attribution collection unit 53 for collecting the value indicating the attribution of the information processing device 60; an object attribution collection unit 54 for collecting the value indicating the attribution of the software 600; and a determination unit 55 for determining that the software 600 is malware when the value indicating the degree of risk obtained by comparing the risk information 510 and the values collected by the subject attribution collection unit 53 and object attribution collection unit 54 satisfies a criterion.