Stealthy and Sparse False Data Injection Attacks Based on Machine Learning

Abstract

Power grid cyber-physical systems face a variety of cyber-attacks. Machine learning based stealthy false data injection attacks are explored in this paper. To avoid the detection of the Bad Data Detector, two issues are considered in the machine learning based attack strategy. One is how to use machine learning to generate suitable attack vectors based on eavesdropping measurements. The other is how to improve the robustness of the attack strategy if there are corrupted measurements or outliers. Considering these two problems, a robust linear regression based false data injection attack strategy is proposed. Moreover, a more sparse attack strategy is also explored to further reduce the cost of attackers. Simulations conducted on the IEEE 14-bus system verify the effectiveness of the attack strategies.