Next Article in Journal
Effect of Control Measures on Wheel/Rail Noise When the Vehicle Curves
Previous Article in Journal
Polyvinyl Alcohol/Lithospermum Erythrorhizon Nanofibrous Membrane: Characterizations, In Vitro Drug Release, and Cell Viability
Article Menu
Issue 11 (November) cover image

Export Article

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

Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization

Science and Technology on Complex Electronic System Simulation Laboratory, Equipment Academy, Beijing 101416, China
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)
View Full-Text   |   Download PDF [5979 KB, uploaded 6 November 2017]   |  


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

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top