Abstract

When establishing a network attack strategy, target network information is not certain, and the attacker lacks comprehensive, reliable and real-time attack information, making it difficult to perform an attack. To address this issue, a complex scientific network attack method is proposed. The attacker’s income, losses, costs and encountered risks related to a cyberattack are analysed, an index system is established, and a dynamic Bayesian network is used to comprehensively assess the attack effects on network nodes to overcome drawbacks of the traditional node importance assessment method, which relies on a single network topological index or makes static assessments of the target node. A simulation experiment shows that the proposed method synthesizes more node information and observed data for the attack, thereby avoiding the discrepancy between actual attack effects and theoretical expectations of attacks from static assessment and delivering higher levels of attack accuracy and efficiency than previous methods.

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