Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization

作者：Sun, Yang^*; Xiong, Wei; Yao, Zhonghua; Moniz, Krishna; Zahir, Ahmed

来源：Applied Sciences-Basel, 2017, 7(11): 1138.

DOI：10.3390/app7111138

• Pareto front
• Q-learning
• multi-objective optimization
• network security

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.

• 出版日期2017-11
• 单位北京邮电大学