Real-Time Detection of Hybrid and Stealthy Cyber-Attacks in Smart Grid

Abstract

For a safe and reliable operation of the smart grid, timely detection of cyber-attacks is of critical importance. Moreover, considering smarter and more capable attackers, robust detection mechanisms are needed against a diverse range of cyber-attacks. With these purposes, we propose a robust online detection algorithm for (possibly combined) false data injection (FDI) and jamming attacks, that also provides online estimates of the unknown and time-varying attack parameters and recovered state estimates. Further, considering smarter attackers that are capable of designing stealthy attacks to prevent the detection or to increase the detection delay of the proposed algorithm, we propose additional countermeasures. Numerical studies illustrate the quick and reliable response of the proposed detection mechanisms against hybrid and stealthy cyber-attacks.

References in corpus (3)

• Machine Learning Methods for Attack Detection in the Smart Grid
• Efficient Strategies for Single/Multi-Target Jamming on MIMO Gaussian Channels
• Optimal Jammer Placement in Wireless Localization Systems

Cited by in corpus (6)

• Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach
• Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids
• Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids
• Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings
• A Stream Learning Approach for Real-Time Identification of False Data Injection Attacks in Cyber-Physical Power Systems
• Multi-Sensor Sequential Change Detection with Unknown Change Propagation Pattern