Signal Decomposition for Intrusion Detection in Reliability Assessment in Cyber Resilience

The objective of this project is to research, assess, and implement machine learning and artificial intelligence and physics-based algorithms for signal decomposition and provide a straightforward framework wherein an anomaly detection algorithm can be trained on existing expected data and then used for false data injection detection. An advanced library for signal decomposition and analysis will be developed that allows combining machine learning and artificial intelligence algorithms and high-fidelity model comparisons for greatly improved false data injection detection. This library will facilitate online and posteriori analysis of digital signals for the purpose of detecting potential malicious tampering in physical processes.