Selection of Network Defense Strategies Based on Stochastic Game and Tabu Search

被引：0
|
作者：
Sun Q. [1 ,2 ]
Xue L. [2 ]
Gao L. [2 ,3 ]
Wang H. [2 ]
Wang Y. [1 ]
机构：
[1] Contemporary Educational Technology Center, Northwest University, Xi'an
[2] State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi'an
[3] State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, College of Computer Science, Xi'an Polytechnic University, Xi'an
来源：
Xue, Leiqi (201832066@stumail.nwu.edu.cn) | 1600年 / Science Press卷 / 57期
基金：
中国国家自然科学基金;
关键词：
Bounded rationality; Defense strategy; Network attack and defense; Stochastic game; Tabu search;
D O I：
10.7544/issn1000-1239.2020.20190870
中图分类号：
学科分类号：
摘要：
The network defence strategy is the key factor to determine the effect of network security protection. In terms of the rational precondition of the existing network defence decision-making research and the parameter selection of the attack and defence benefit function, there are model deviations for the factors such as information asymmetry and legal punishment in the actual network attack and defence, which reduces the practicability and reliability of the strategy. In this paper, the Tabu random game model is constructed on the basis of the preconditions of bounded rationality, the Tabu search algorithm is introduced to analyze the bounded rationality of random game, and a search algorithm with memory function is designed. The data structure of the Tabu table is used to realize the memory function, and the data-driven memory combined with the game model is used to get the optimal defence strategy. The experimental results show that this method improves the accuracy in the quantification of attack and defence benefits, improves the accuracy of defence benefits compared with the existing typical methods, and the algorithm space complexity is better than the reinforcement learning and other typical algorithms. © 2020, Science Press. All right reserved.
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页码：767 / 777
页数：10
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