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?
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.