Techniques for dynamic server groups that can be patched together using stream clustering algorithms, and learning components in order to reuse the repeatable patterns using machine learning are provided herein. In one example, in response to a first risk associated with a first server device, a risk assessment component patches a server group to mitigate a vulnerability of the first server device and a second server device, wherein the server group is comprised of the first server device and the second server device. Additionally, a monitoring component monitors data associated with a second risk to the server group to mitigate the second risk to the server group.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support providing secure backup and recovery of files from edge devices during ransomware attacks or other cyberattacks. Secure data, such as medical records, may be stored at one or more networked storage nodes and backup images (e.g., snapshots) may be stored at a disconnected storage node (e.g., an air-gapped storage node) that is isolated from the networked storage nodes. Application programming interface (API) calls may be managed and monitored to detect an alarm state (e.g., a ransomware attack), and based on the alarm state, storage and retrieval from the networked storage nodes may be stopped. Additionally, a recent backup image from the disconnected storage node may be retrieved for use in performing system recovery operations.

An electronic device is disclosed. An electronic device according to various embodiments comprises: a processor; and a memory electrically connected to the processor, wherein the processor may be configured to: obtain a plurality of first parameters associated with attributes of at least one malicious code and a plurality of second parameters associated with a system in which the at least one malicious code is executed; obtain a similarity on the basis of a first comparison result according to a first comparison method between the plurality of first parameters and a second comparison result according to a second comparison method between the plurality of second parameters; and classify the at least one malicious code into at least one cluster on the basis of the similarity between the at least one malicious code. Other various embodiments may be provided.

Systems, methods, devices, and computer readable media related to fraud detection. Fraud detection is achieved using a flexible scripting language and syntax that simplifies the generation of fraud detection rules. The rules are structured as conditional IF-THEN statements that include data objects referred to as Anchors and Add-Ons. The Anchors and Add-Ons used to generate the rules also correspond to a distinct data path for the retrieval data from any of a variety of data sources. The retrieval of data from the various data sources is optimized based on data dependencies within the rules. By knowing the data dependencies of each rule and utilizing parallelization of rule execution, the retrievals of data from the data sources is achieved efficiently so the rules can be executed quickly.

A discrete three-dimensional (3-D) processor comprises stacked first and second dice. The first die comprises 3-D memory (3D-M) arrays, whereas the second die comprises logic circuits and at least an off-die peripheral-circuit component of the 3D-M array(s). In one preferred embodiment, the first and second dice are vertically stacked. In another preferred embodiment, the first and second dice are face-to-face bonded.

A discrete three-dimensional (3-D) processor comprises communicatively coupled first and second dice. The first die comprises memory arrays, whereas the second die comprises at least a non-memory circuit and at least an off-die peripheral-circuit component of the memory arrays. The first and second dice have substantially different structures, more particularly back-end-of-line (BEOL) structures.

A system and method for malware classification using machine learning models trained using synthesized feature sets based on features extracted from samples of known malicious objects and known safe objects. The synthesized feature sets act as virtual samples for training a machine learning classifier to recognize new objects in the wild that are likely to be malicious.

Disclosed are a computer system, a signature verification server, a method of supporting signature verification by a computer system, and a method of verifying signature. Embodiments of the present disclosure relates to a technology of misdiagnosis verification of signature used for a malicious code diagnosis, and more particularly to technologies which derive a result of performance of a malicious code diagnosis simulation on signature in a multi-user computer environment to use an actual client antivirus software and thus can overcome physical, spatial, and temporal limitations of conventional signature misdiagnosis verification by pre-distributing preliminary application signature in a state where misdiagnosis verification has not been completed to a plurality of user computers to reflect the preliminary application signature to a malicious code diagnosis on files stored in the plurality of user computers and performing misdiagnosis verification on the preliminary application signature based on information collected in connection with a result of the diagnosis.

A computer-implemented method, computer program product and computing system for: obtaining hardware performance information concerning hardware deployed within a computing platform; obtaining platform performance information concerning the operation of the computing platform; obtaining application performance information concerning one or more applications deployed within the computing platform; and generating a holistic platform report concerning the computing platform based, at least in part, upon the hardware performance information, the platform performance information and the application performance information.

Various embodiments discussed herein enable the detection of malicious content. Some embodiments do this by determining a similarity score between content, computer objects, or indications (e.g., vectors, file hashes, file signatures, code, etc.) known to be malicious and other content (e.g., unknown files) or indications based on feature weighting. Over various training stages, certain feature characteristics for each labeled malicious content or indication can be learned. For example, for a first malware family of computer objects, the most prominent feature may be a particular URL, whereas other features change considerably for different iterations of the first malware family of computer objects. Consequently, the particular URL can be weighted to determine a particular output classification corresponding to malicious behavior.