Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution System

The increased integration of distributed energy resources (DERs) in Active Distribution Systems (ADS)enhances grid flexibility but also introduces new cybersecurity vulnerabilities. Cyber-attacks on such systemscan disrupt grid operations, leading to energy instability or blackouts. This paper proposes an adaptivehierarchical framework for cyber-attack detection and localization in ADS. The proposed system combinesdeep learning with graph-based spectral clustering and statistical impact scoring to identify and locate cyberintrusions in near-real-time. Detection is achieved using a sequential deep neural network trained on electricalsignal anomalies, while localization is carried out in two stages—first, using spectral clustering for regionaldetection, and then using waveform-based metrics for precise node identification. The system’s performanceis validated using simulation data from IEEE 37-node feeders with injected false data and coordinated attackscenarios. Results demonstrate superior detection accuracy, localization precision, and scalability comparedto conventional intrusion detection systems. This work contributes toward building a resilient smart grid byenabling real-time situational awareness and rapid response