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Nuclear Power Plant Instrumentation and Control Cyber Security Common Vector Access Leading to Relational Common Cause Failures.
Abstract not provided.
Smart Grid Design Development and Cyber Security for Small Modular Reactors.
Abstract not provided.
Nuclear Power Plant Instrumentation and Control Cyber Security Common Vector Access Leading to Relational Common Cause Failure.
Abstract not provided.
Cyber Security Research and Development – CAN Bus Security Research Across Multiple Sectors
This paper will detail the general findings of the INL research team while performing security assessments of 3 different vehicles: (1) a common domestic passenger vehicle, (2) a Japanese Plug-in Electric Vehicle (PEV), and (3) a flagship German sedan. A contrast of these vehicles and how CAN was implemented, what capabilities were available via CAN, and an overview of the level of complexity and difficulty of compromise will be summarized.
Interesting Facts on Cyber Security
Additional information on our findings for our research project. We were not able to fit this on our poster but we would like to share the information with the public while doing our presentation.
Advanced Cyber-secured Protection Schemes for Renewable-rich Microgrids.
Abstract not provided.
Simulation of Workflow and Threat Characteristics for Cyber Security Incident Response Teams.
Abstract not provided.
Deep Learning for Unsupervised Insider Threat Detection in Structured Cyber Security Data Streams
Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability to aid analysts reviewing potential cases of insider threat. Using the CERT Insider Threat Dataset v6.2 and threat detection recall as our performance metric, our novel deep and recurrent neural network models outperform Principal Component Analysis, Support Vector Machine and Isolation Forest based anomaly detection baselines. For our best model, the events labeled as insider threat activity in our dataset had an average anomaly score in the 95.53 percentile, demonstrating our approach's potential to greatly reduce analyst workloads.