Security weakness of dynamic watermarking-based detection for generalised replay attacks

Changda Zhang, Dajun Du, Qing Sun, Xue Li, Aleksandar Rakić and Minrui Fei

International Journal of Systems Science, 2022, vol. 53, issue 5, 948-966

Abstract: Dynamic watermarking (DW)-based detection methods can effectively detect replay attacks, however these detection methods cannot achieve the desired effectiveness for generalised replay attacks (GRAs). The objectiveness of this paper is to investigate security weakness of DW-based detection for GRAs. Firstly, unlike replaying history data from replay attacks, data accuracy compromised by GRAs is analysed, where their characteristics are similar to that compromised by replay attacks, and the current data are distorted by typical GRAs such as injection, replacement and scaling attacks. Secondly, considering the corresponding hidden Markov models of different types of GRAs, the stealthiness is quantified by auto covariance of residuals and cross covariance between residuals and watermarking signals. It is found that if both auto and cross covariance keep unchanged or increase along with watermarking intensity increasing, GRAs cannot be detected effectively. Thirdly, after DW-based detection is invalid for GRAs, it is proved that the stability of closed-loop systems for open-loop unstable dynamics will be destroyed, or the admissible system state bounds can be crossed within a finite time. Finally, experiments are operated on a networked inverted pendulum platform, and experimental results confirm security weakness of DW-based detection for GRAs.

Date: 2022
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DOI: 10.1080/00207721.2021.1979687

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