标题:Intrusion detection algorithms based on correlation information entropy and binary particle swarm optimization
作者:Wang, Yan-Fei ;Liu, Pei-Yu ;Ren, Min ;Chen, Xiao-Xue
通讯作者:Liu, Pei-Yu
作者机构:[Wang, Yan-Fei ;Liu, Pei-Yu ;Chen, Xiao-Xue ] School of Information Science and Engineering, Shandong Normal University, Shandong Provincial Key Labor 更多
会议名称:13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
会议日期:July 29, 2017 - July 31, 2017
来源:ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
出版年:2017
页码:2829-2834
DOI:10.1109/FSKD.2017.8393229
摘要:In current intrusion detection, redundant features often lead to the degradation of detection accuracy. Aiming at this problem, an intrusion detection algorithm based on correlation information entropy and binary particle swarm optimization algorithm was proposed. Correlation information entropy was used to sort features. This can filter irrelevant features. So the feature dimension was reduced. Then some better subsets that were gotten from feature sorting were used as the part initial population. In this way, the following particle swarm optimization algorithm would have a good starting point. The test results showed that the better classification performance was obtained according to the selected optimal feature subset, and the testing time of the system was reduced effectively.
© 2017 IEEE.
收录类别:EI
资源类型:会议论文
TOP