Ut-battelle, Inc.

Jared M Smith

Robert A Bridges

Kiren E Verma

Jeffrey A Nichols

Jordan A Johnson

Berat E Arik

Benjamin T McDaniel

Timothy S Oesch

Oumar Souleymane Diallo

Christopher R Hathhorn

Luke Koch

Brian Weber

Rachel L Petrik

Whether testing intrusion detection systems, conducting training exercises, or creating data sets to be used by the broader cybersecurity community, realistic user behavior is a desirable component of a cyber-range. Existing methods either rely on network level data or replay recorded user actions to approximate real users in a network. Probabilistic models can be fit to actual user data (sequences of application usage) collected from endpoints. Once trained to the user's behavioral data, these models can generate novel sequences of actions from the same distribution as the training data. These sequences of actions can be fed to emulator software via configuration files, which replicate those behaviors on end devices. The models are platform agnostic and can generate behavior data for any emulation software package. In some embodiments a latent variable is added to faithfully capture and leverage time-of-day trends.

Data driven computer user emulation

An adaptive malware writing system includes a targeting engine that classifies malware candidates as a malicious candidate or a benign candidate through a surrogate model. The surrogate model assigns a weight to each byte of the malware candidates through a saliency vector. The sum of the weights render a malware classification score. An alteration engine alters a binary form of the malware candidates classified as malware by executing a functional analysis that traces application program interface calls and memory. The alteration engine alters the binary form of the malware candidates classified as malware to render a synthesized malware. The malware analysis determines if the synthesized malware is operational by comparing an image of the synthesized malware to an image of at least one of the plurality of malware candidates. A target classifier engine identifies the vulnerabilities of a targeted computer.

Adaptive malware binary rewriting

One or more virtual supervisory control and data acquisition (SCADA) controllers are provided. The virtual SCADA controller(s) may be deployed upon a detection of a failure of a physical SCADA controller. An on-site module may detect failure including a security breach which causes the physical SCADA controller to be disconnected from a network and the virtual SCADA controller(s) to take over control. The virtual SCADA controller(s) may be on-site or off-site.

Virtual supervisory control and data acquisition (SCADA) automation controller

A system and method (referred to as the system) detect malware by training a rule-based model, a functional based model, and a deep learning-based model from a memory snapshot of a malware free operating state of a monitored device. The system extracts a feature set from a second memory snapshot captured from an operating state of the monitored device and processes the feature set by the rule-based model, the functional-based model, and the deep learning-based model. The system identifies identifying instances of malware on the monitored device without processing data identifying an operating system of the monitored device, data associated with a prior identification of the malware, data identifying a source of the malware, data identifying a location of the malware on the monitored device, or any operating system specific data contained within the monitored device.

Independent malware detection architecture

A system and method detects malware by processing notifications from an intrusion detection system and baseline snapshots from an image capture utility. The image capture utility constructs an image of the suspected malware intrusion and links the suspected malware intrusion to the baseline snapshots. The system and method propagates the image of the suspected malware intrusion across multiple networks before it distinguishes malicious code, device state, and files from benign code, device state, and files. Some systems and methods include a malware recovery system that executes machine learning instructions and heuristics to revert a client and/or a remote server to one or more baseline snapshots.

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Oak Ridge, TN, United States of America

Jared M Smith