A Novel Model of Mimic Defense Based on Minimal L-Order Error Probability

Abstract

Mimic defense is an active defense theory, which aims to fundamentally change the “easy to attack and difficult to defend” situation of network security. In this paper, we propose an evaluation method based on the probability of being attack successfully, and improve the evaluation scheme of historical confidence. We combine the two evaluation schemes with the TOPSIS (technique for order performance by similarity to ideal solution) algorithm, and finally form a complete heterogeneous variant dynamic scheduling model. Different from traditional multi-mode voting algorithms, the effect of the heterogeneous degree in voting is considered, and we use Bayesian estimation to obtain the optimal result in the probabilistic sense. Finally, simulation results show that the proposed algorithm can effectively enhance the dynamic and security of the mimic defense model, and give full play to the characteristics of mimic defense.

Keywords

• Mimic defense
• heterogeneity
• dynamic scheduling
• TOPSIS algorithm
• Bayesian estimation