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Security and Communication Networks
Volume 2017 (2017), Article ID 9602357, 12 pages
Research Article

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

1Department of Computer Science, University of Hong Kong, Pokfulam Road, Hong Kong
2Peking University, Beijing, China

Correspondence should be addressed to Jingxuan Wang

Received 10 April 2017; Accepted 7 June 2017; Published 10 August 2017

Academic Editor: Leo Y. Zhang

Copyright © 2017 Jingxuan Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.