Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization

被引：7
作者：
Sun, Yang [1 ]
Xiong, Wei [1 ]
Yao, Zhonghua [1 ]
Moniz, Krishna [2 ]
Zahir, Ahmed [2 ]
机构：
[1] Equipment Acad, Sci & Technol Complex Elect Syst Simulat Lab, Beijing 101416, Peoples R China
[2] Beijing Univ Posts & Telecommun, Key Lab Trustworthy Distributed Comp & Serv, Beijing 100876, Peoples R China
来源：
APPLIED SCIENCES-BASEL | 2017年 / 7卷 / 11期
关键词：
Pareto front; Q-learning; multi-objective optimization; network security; FLOW-CONTROL; MODEL;
D O I：
10.3390/app7111138
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
Improving network security is a difficult problem that requires balancing several goals, such as defense cost and need for network efficiency, in order to achieve proper results. In this paper, we devise method of modeling network attack in a zero-sum multi-objective game and attempt to find the best defense against such an attack. We combined Pareto optimization and Q-learning methods to determine the most harmful attacks and consequently to find the best defense against those attacks. The results should help network administrators in search of a hands-on method of improving network security.
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页数：24
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