Battelle Memorial Institute

Other

Thomas E Carroll

Thomas W Edgar

Satish Chikkagoudar

Garret E Seppala

Bora A Akyol

Michael E Mylrea

Ralph J Cataneo

Lars J Kangas

Jereme Nathan Haack

Penny L McKenzie

Kristine M Arthur-Durett

Kiri J Oler

Dennis G Thomas

Kristine M Arthur

Daniel M Johnson

Kyle E Monson

Keith W Thornhill

Computing system operational methods and apparatus are described. According to one aspect, a computing system operational method includes accessing user information regarding a user logging onto a computing device of the computing system, processing the user information to determine if the user information is authentic, as a result of the processing determining that the user information is authentic, first enabling the computing device to execute an application segment, and as a result of the processing determining that the user information is authentic, second enabling the application segment to communicate data externally of the computing device via one of a plurality of network segments of the computing system.

Computing system operational methods and apparatus

A cybersecurity infrastructure command validation system is provided herein for validating asset commands issued within an infrastructure network. The cybersecurity infrastructure command validation system can be integrated into an infrastructure network to monitor and validate infrastructure asset commands in real-time or while the infrastructure network is active. The cybersecurity infrastructure command validation system can receive or intercept commands issued by asset controllers. The cybersecurity infrastructure command validation system can validate the commands based on a command validation model. The command validation model can represent normal operating behavior of the infrastructure network. The cybersecurity infrastructure command validation system can provide valid commands to the intended infrastructure asset, or can reject invalid commands. The cybersecurity infrastructure command validation system can store validation results for use in updating the command validation model. The cybersecurity infrastructure command validation system can flag or otherwise warn the infrastructure network or administrators of invalid commands.

Integrated behavior-based infrastructure command validation

A network analysis tool receives network flow information and uses deep learning&#x2014;machine learning that models high-level abstractions in the network flow information&#x2014;to identify dependencies between network assets. Based on the identified dependencies, the network analysis tool can discover functional relationships between network assets. For example, a network analysis tool receives network flow information, identifies dependencies between multiple network assets based on evaluation of the network flow information, and outputs results of the identification of the dependencies. When evaluating the network flow information, the network analysis tool can pre-process the network flow information to produce input vectors, use deep learning to extract patterns in the input vectors, and then determine dependencies based on the extracted patterns. The network analysis tool can repeat this process so as to update an assessment of the dependencies between network assets on a near real-time basis.

Extracting dependencies between network assets using deep learning

A network analysis tool evaluates network flow information in complementary workflows to identify one-hop behavior of network assets and also identify multi-hop dependencies between network assets. In one workflow (e.g., using association rule learning), the network analysis tool can identify significant one-hop communication patterns to and/or from network assets, taken individually. Based on the identified one-hop behavior, the network analysis tool can discover patterns of similar communication among different network assets, which can inform decisions about deploying patch sets, mitigating damage, configuring a system, or detecting anomalous behavior. In a different workflow (e.g., using deep learning or cross-correlation analysis), the network analysis tool can identify significant multi-hop communication patterns that involve network assets in combination. Based on the identified multi-hop dependencies, the network analysis tool can discover functional relationships between network assets, which can inform decisions about configuring a system, managing critical network assets, or protecting critical network assets.

Complementary workflows for identifying one-hop network behavior and multi-hop network dependencies

A skin refining composition contains five surfactants: sodium lauryl ether sulfate, cocoamidopropylbetaine, decyl polyglucoside, sucrose cocoate, and an amphoteric detergent. The composition also contains a sub-micron conditioning emulsion. The emulsion contains a silicone, a moisturizer, a humectant, and glycerin. The composition cleanses, exfoliates, and conditions the skin. The composition is applied with an applicator containing a foam pad and an integral brush having tapered bristles.

Skin refining composition and applicator

Helmet lamp

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Richland, WA, United States of America

Thomas E Carroll