Feature selection algorithm based on improved particle swarm joint taboo search

• Published in: Tongxin Xuebao Vol. 39; pp. 60 - 68
• Main Authors: Zhen ZHANG, Peng WEI, Yufeng LI, Julong LAN, Ping XU, Bo CHEN
• Format: Journal Article
• Language: Chinese
• Published: Editorial Department of Journal on Communications 01.12.2018
• Subjects: feature selection; genetic algorithm; intrusion detection; particle swarm optimization; taboo search
• ISSN: 1000-436X

To solve the problem of high data feature dimensionality in intrusion detection, a feature selection algorithm based on improved particle swarm optimization taboo search (IPSO-TS) was proposed. The genetic algorithm was used to improve the particle swarm optimization, and the initial optimal solution of feature selection was obtained. A taboo search (TS) algorithm was used for initial optimal solution to obtain the global optimal solution of the feature subset. Compared with genetic algorithm integrated particle swarm optimization (CMPSO), particle swarm optimization (PSO) and PSO-TS algorithms, experimental results based on the KDD CUP 99 dataset show that the method reduces the features by about 29.2% , shortens about 15% of the average detection time, and increases about 2.96% of the average classification accuracy.