Attack Simulation and Mitigation in Smart Grid Cybersecurity Environments

Abstract

Smart grids integrate information and communication technologies with power systems to enhance efficiency, reliability, and sustainability. However, this connectivity also introduces significant cybersecurity risks, making them vulnerable to sophisticated cyber attacks that can disrupt operations, compromise sensitive data, and threaten public safety. This manuscript presents a comprehensive study on attack simulation and mitigation strategies within smart grid cybersecurity environments. We design and implement a modular testbed emulating advanced metering infrastructure (AMI), distribution management systems (DMS), and supervisory control and data acquisition (SCADA) networks to assess attack vectors such as false data injection (FDI), denial-of-service (DoS), and replay attacks under realistic load conditions.

Our methodology combines rule-based thresholds with a machine learning–based Random Forest classifier to detect anomalies, while response protocols leverage rapid node isolation, dynamic network reconfiguration, and conservative dispatch safeguards. Statistical analysis evaluates detection accuracy, false-positive rates, mitigation latency, and availability retention across scenarios. Results show the hybrid framework achieves over 95% detection accuracy, false positives under 4.5%, and average mitigation latency of 2.07 seconds, preserving over 90% of normal operations. We discuss practical insights for utilities, outline best practices, and identify future research directions focused on adaptive learning and large-scale validation.