Stealthiness Analysis of the Identified Model-Based Covert Attacks with Unknown Model

Dajun Du, Changda Zhang, Jin Zhang, Qing Sun, Minrui Fei, Huiyu Zhou

Research output: Contribution to journalArticlepeer-review


Most of existing covert attacks are achieved based on known model, which is however impractical for the attacker, leading to the failure of covert attacks. To solve this problem, this paper presents a novel covert attacks method with unknown model. Firstly, the negative correlation between the model errors and the stealthiness of traditional covert attacks (TCAs) is quantitatively analyzed, where bigger errors brings worse stealthiness. Secondly, the subspace identification method is used to obtain the identified model of the plant. Furthermore, a novel identification and adaptive compensation based covert attacks (IACCAs) method is proposed, which can adaptively compensate the attack signals by adaptive law. Although there exist the identification errors, the stealthiness of IACCAs is proved to be similar as TCAs with known model. Finally, experimental results demonstrate the feasibility and effectiveness of the proposed IACCAs method, which can achieve covert attacks with unknown model.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Control of Network Systems
StateAccepted/In press - 2023
Externally publishedYes


  • Adaptation models
  • Analytical models
  • Control systems
  • Covert attacks
  • Cyberattack
  • Denial-of-service attack
  • Detectors
  • Network systems
  • adaptive compensation
  • identification
  • networked control systems


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