Permutation entropy based detection scheme of replay attacks in industrial cyber-physical systems

Autor: Zhengdao Zhang, Linbo Xie, Mei Zhou
Rok vydání: 2021
Předmět:
Zdroj: Journal of the Franklin Institute. 358:4058-4076
ISSN: 0016-0032
DOI: 10.1016/j.jfranklin.2021.02.024
Popis: Although data integrity attack detections are critical to cyber-physical systems (CPSs), replay attack detection in industrial CPSs, especially data-based detection methods against replay attacks, has not been well-studied. Because it is difficult to distinguish replayed historical measurements and current measurements, replay attacks are hard to detect. In this paper, we propose a permutation entropy based detection scheme according to the complexity characteristics of sensor measurements. As sensor measurements generated during replay attacks present some sort of regularity, the significant decreasing of permutation entropy indicates the occurrence of replay attacks. Support vector data description (SVDD) is used to classify replay attacks according to permutation entropy of sensor measurements. Considering the manipulative attacker who mixes gaussian white noise or gaussian colored noise with measurements to bypass the entropy detection, we use wavelet analysis to denoise the measurements in advance. Finally, we demonstrate the efficiency of the proposed method through its application to a semi-physical simulation testbed. The experiment results show that our method can detect replay attacks accurately.
Databáze: OpenAIRE