Resilience Enhancement of Smart Power Systems Against False Data Injection Attacks Using Adaptive Intrusion Detection Mechanisms

The integration of Cyber-Physical Systems (CPS) within smart power grids has brought significant improvements in monitoring and controlling these systems. However, it has also increased the cyber-security and privacy risks for the grids. This article introduces a new way to improve the security and privacy of smart power systems through an adaptive intrusion detection mechanism. This mechanism is designed to tackle the specific challenges that CPS environments present, using real-time data processing and adaptive intrusion detection mechanisms. It can effectively identify both known and new cyber threats with high accuracy. A strong method is outlined to enhance the detection and reduction of false data injection attacks (FDIAs) that target smart power grids. This approach helps identify targeted attacks, even when anomalies are minimal, thereby ensuring better system resilience, operational stability, and data privacy protection. This adaptive framework offers a promising solution to boost the resilience of CPS in power systems. The effectiveness of the proposed technique is assessed using IEEE 14-bus, IEEE 39-bus, and 118-bus systems in the MATLAB (R2023b) environment. It measures resilience and privacy leakage caused by FDIA in two scenarios: delayed detection with high residual values and timely detection with low residual values. The simulation results showcase the effectiveness of this strategy for secure grid operation.