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F-DDIA: A Framework for Detecting Data Injection Attacks in Nonlinear Cyber-Physical Systems

Data injection attacks in a cyber-physical system aim at manipulating a number of measurements to alter the estimated real-time system states. Many researchers recently focus on how to detect such attacks. However, most of the detection methods do not work well for the nonlinear systems. In this paper, we present a compressive sampling methodology to identify the attack, which allows determining how many and which measurement signals are launched. The sparsity feature is used. Generally, our methodology can be applied to both linear and nonlinear systems. The experimental testing, which includes realistic load patterns from NYISO with various attack scenarios in the IEEE 14-bus system, confirms that our detector performs remarkably well.