ACO and SVM Selection of Network Intrusion Detection Method 

Jianjun WU 

Hunan City University, Yiyang, Hunan, 413000, China

Abstract: Feature selection and classifier design is the key to network intrusion detection. In order to improve network intrusion detection rate for feature selection problem, this paper proposed a network intrusion detec-tion method (ACO-FS -SVM) combining ant colony algorithm to ｓｅｌｅｃｔ the features with a feature weighting SVM. First, the use of support vector machine classification accuracy and feature subset dimension construct a comprehensive fitness weighting index. Then use the ant colony algorithm for global optimization and mul-tiple search capabilities to achieve optimal solutions feature subset search feature. And then selected the key feature of network data and calculated information gain access to various features weights and heavy weights to build support vector machine classifier based on the characteristics of network attacks right. At last, refine the final design of the local search methods to make the feature selection results without redundant features while improve the convergence resistance, and verify the data set by KDD1999 effectiveness of the algorithm. The results show that ACO-FS-SVM can effectively reduce the dimension of features, and have improved network intrusion detection accuracy and detection speed. 

Keywords: Feature Selection; Feature Weighting; Ant Colony Optimization Algorithm; Support Vector Machines; Network Intrusion Detection