Adversarial Hiding Deception Strategy and Network Optimization Method for Heterogeneous Network Defense

Abstract

Heterogeneous networks are powerful tools for describing different types of entities and relationships and are more relevant models of complex networks. The study of heterogeneous network defense is of great practical significance for protecting useful networks such as military combat networks and critical infrastructure networks. However, a large amount of current research on complex network defense focuses on homogeneous networks under complete information conditions, which often ignore the real conditions such as incomplete information and heterogeneous networks. In this paper, we propose firstly a new adversarial hiding deception strategy for heterogeneous network defense under incomplete information conditions. Secondly, we propose an adversarial hiding deception network optimization method based on a genetic algorithm and design node importance index and a fitness function, which take into account the graph structure information and information about the type of nodes. Finally, we conduct comparison experiments for different defense strategies, and the results show that the proposed strategy and network optimization method are effective at hiding the critical nodes and inducing the attacker to attack the non-important nodes. The generated adversarial hiding deception network has a similar graph structure to the real network.