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Appl. Sci. 2017, 7(11), 1138; https://doi.org/10.3390/app7111138

Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization

1
Science and Technology on Complex Electronic System Simulation Laboratory, Equipment Academy, Beijing 101416, China
2
Key Laboratory of Trustworthy Distributed Computing and Service, Beijing University of Posts and Telecommunications, Beijing 100876, China
*
Author to whom correspondence should be addressed.
Received: 10 October 2017 / Revised: 29 October 2017 / Accepted: 30 October 2017 / Published: 6 November 2017
(This article belongs to the Section Computer Science and Electrical Engineering)
Full-Text   |   PDF [5979 KB, uploaded 6 November 2017]   |  

Abstract

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. View Full-Text
Keywords: Pareto front; Q-learning; multi-objective optimization; network security Pareto front; Q-learning; multi-objective optimization; network security
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sun, Y.; Xiong, W.; Yao, Z.; Moniz, K.; Zahir, A. Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization. Appl. Sci. 2017, 7, 1138.

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