Novel attack-defense framework for nonlinear complex networks: An important-data-based method

This article addresses the state estimation problem for a class of nonlinear complex networks (CNs) under attack. First, a novel important-data-based (IDB) attack strategy is skillfully proposed from the adversary's point of view to maximize the attack effect. Different from most existing attack models, the IDB attacker has the ability to eavesdrop measurements and only attacks the packets which play an important role in the system. As such, a larger system performance degradation can be expected. Second, a new kind of resilient H & INFIN;$$ {H}_{\infty } $$ estimator is designed, from the perspective of the defenders, to alleviate the negative effect of the attack. In a word, a novel unified attack-defense framework for nonlinear CNs is established. In order to make up for the defect that the IDB attacker's parameter is unknown to the defender, an algorithm is developed to approximate the attack parameter. With the help of the Lyapunov functional method, sufficient conditions are obtained to resist the proposed IDB attack and ensure the H & INFIN;$$ {H}_{\infty } $$ performance of the augmented system. At last, two examples are given to demonstrate the destructiveness of the proposed IDB attack strategy and the effectiveness of the developed resilient H & INFIN;$$ {H}_{\infty } $$ estimator, respectively.