Does the implementation of machine-learning-based anomaly detection increase the risk of system latency and false-positive trips in automated smart grid controllers compared to traditional regex-based filtering?
This Extended Project Qualification investigates whether machine-learning-based anomaly detection systems introduce greater operational risk in automated smart grid protection controllers, that is, in latency and false-positive circuit-trip rates, than traditional regex-based and signature-based filtering. This project compares three detection methods, including Snort-based rule filtering, an Isolation Forest classifier, and an LSTM Autoencoder using a primary data based on an analysis of the bachirbarika Power System data, a testbed PMU and SCADA dataset consisting of 78,369 rows and 15 different attack scenarios with assistance provided by a review of peer-reviewed literature. Findings indicate that ML-based algorithms identify a significantly higher number of attacks compared to Snort in a setup where the attack is shown as a physical-state anomaly, not as an event on the network layer, but at the tradeoff of introducing a quantifiably higher false positive rate and, in the case of the Isolation Forest, a very great curiousness of inference. LSTM Autoencoder has a more refined portrait with similar accuracy on detection at a lower latency to the baseline of the rule-based. The findings indicate that hybrid-based approach to architecture should be suggested, where rule-based filtering is applied to address the time-sensitive deterministic checks, and the ML models give the context-driven anomaly analysis on both the SCADA and the wide-area layers. All the differences in the latencies were statistically significant, as tested with Mann-Whitney U at p < 0.001.