Fourier Series Fitting-Based Intrusion Detection for Real-Time Industrial Control Systems

Recent incidents have shown the attacks against Industrial Control System (ICS), which may result in failure or even catastrophe and prompt the studies of defense. Existing security systems are mostly based on network perimeter defenses which lacking effective countermeasures in the event that such defenses are bypassed and attackers gain access to the system. In this paper, we propose a Fourier series fitting based intrusion detection method, which enables a detection and an alarm when ICS is under attack. Specifically, we first divide the data into three parts based on time with a sliding window, namely real-time data, sliding window data to be predicted, and historical data. Then we fit the data within the prediction window to the data around the window, together with the root mean squared error (RMSE) calculated for the data within the window and the historical data. The intrusion detection system (IDS) will alarm when the difference between the above RMSE and the reference one is greater than a threshold value. We test two types of data injection attacks, extensive simulations illustrate the effectiveness of our method in detecting both of these attacks.