A Probabilistic Data Recovery Framework Against Load Redistribution Attacks Based on Bayesian Network and Bias Correction Method

In the realm of power systems, load redistribution attacks (LRAs) involve the manipulation of measurements by attackers, leading operators to make decisions based on falsified estimated loads. Detecting LRAs is a well-explored area, but the critical post-detection phase, which requires operators to understand the attack's nature, remains under-addressed. Our paper introduces a novel probabilistic mechanism based on the Bayesian network model. It excels at not only detecting affected areas but also pinpointing the buses with manipulated loads under LRAs. Our work prioritizes simplicity by offering a straightforward calculation method that doesn't rely on complex tools or extensive system parameters. It also exhibits robustness against measurement noise. Acknowledging the paramount importance of data recovery, we highlight that post-detection measures are essential for minimizing errors in optimal power flow calculations. However, this data recovery is challenging due to the complexity of understanding the attacker's behavior. Our methodology employs kernel density estimation for precise probability density calculations and integrates the Bayesian Markov model to reduce computational complexity. Rigorous testing on the IEEE 118 bus system demonstrates its strong performance in producing consistently satisfactory results.