FS-SVM Based Intrusion Detection Feature Selection and classification
Feature selection is an important issue in machine learning.Support Vector Machine (SVM) is a popular topic in Intrusion Detection System (IDS) for its abilities to perform classification and regression in recent years. Solving a support vector machine problem is a typical quadratic optimization problem, which is influenced by its time complexity. A FS-SVM algorithm based on Fisher score and SVM was presented in this paper. This algorithm was applied on KDDCUP99 standard Intrusion Detection dataset. The experiment results show, using FS-SVM algorithm to select the important features from the original feature space is an effective method to reduce the dimension of the example feature vector. The classification accuracy has not decrease comparing to the original feature space in general.
FS-SVM Feature Selection Intrusion detection
Xueqin Zhang Chunhua Gu
College of information science and Engineering, East China University of science and technology Shanghai, 200237, China
国际会议
杭州
英文
1084-1086
2006-10-12（万方平台首次上网日期，不代表论文的发表时间）