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

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

Abstract: Most existing covert attacks are achieved based on a known model, which is, however, impractical for the attacker, leading to the failure of covert attacks. To solve this problem, this article presents a novel covert attack method with an unknown model. First, the negative correlation between the model errors and the stealthiness of traditional covert attacks (TCAs) is quantitatively analyzed, where bigger errors bring worse stealthiness. Second, 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 the identification errors exist, the stealthiness of IACCAs is proven to be similar to TCAs with a known model. Finally, experimental results demonstrate the feasibility and effectiveness of the proposed IACCAs method, which can achieve covert attacks with an unknown model.