An Adversarial Approach to Evaluating the Robustness of Event Identification Models

arXiv:2402.12338v1 Announce Type: cross
Abstract: Intelligent machine learning approaches are finding active use for event detection and identification that allow real-time situational awareness. Yet, such machine learning algorithms have been shown to be susceptible to adversarial attacks on the incoming telemetry data. This paper considers a physics-based modal decomposition method to extract features for event classification and focuses on interpretable classifiers including logistic regression and gradient boosting to distinguish two types of events: load loss and generation loss. The resulting …

abstract adversarial adversarial attacks algorithms arxiv attacks cs.cr cs.lg cs.sy data detection eess.sy event identification intelligent machine machine learning machine learning algorithms modal paper physics real-time robustness situational awareness telemetry telemetry data type