A Probabilistic-Based Approach for Detecting Simultaneous Load Redistribution Attacks Through Entropy Analysis and Deep Learning

Load redistribution attacks (LRAs) are one of the most sneaky and realistic types of false data injection attacks (FDIAs), in which the attacker manipulates the measurements in a way that depicts a false image of the system situation for the operator. Due to the uncertainty in the system’s parameters, system modeling (AC or DC), and so on, detection LRAs have a lot of challenges. To overcome the difficulty of devising a general mechanism for LRA detection based on deterministic methods, we propose a probabilistic approach based on entropy analysis and deep learning. The ratio of cyber loads to real loads (RCLRLs) is the major input of the proposed detection algorithm to make the presented method applicable for different load levels in the system. By extracting the entropy of RCLRLs under LRAs, our method reduces dependency on system modeling and the system’s parameters. We employ the bias correction method on forecasted loads to approximate the real load in the system, enhancing our approach’s accuracy. The framework is a decentralized algorithm that detects simultaneous LRAs in different areas and ensures scalability for large systems. Simulations on the IEEE 118-bus systems demonstrate the proposed method’s high accuracy and rapid response.