Title :
Training Samples Selection Method in Intrusion Detection System
Author :
Shuyan, Cao ; Li, Zhang
Author_Institution :
Sch. of Inf. Technol. & Manage. Eng., Univ. of Int. Bus. & Econ., Beijing, China
Abstract :
Taking the example of designing classifier in intrusion detection system, this paper studies on samples selection problem for classifier and proposes a method fitting for large data set. First, use cluster analysis and the information known of classification to select boundary samples of each class. Then cluster for each class of the remaining non-border samples and adopt the method based on sample density to delete samples in each cluster. As reserving border samples and reducing training samples, it can guarantee generalization performance and training efficient of the classifier.
Keywords :
pattern classification; pattern clustering; security of data; very large databases; boundary samples classification; cluster analysis; intrusion detection system; large data set; sample density; samples selection; Data engineering; Engineering management; Information management; Information technology; Intrusion detection; Management training; Nearest neighbor searches; Technology management; Training data; Virtual colonoscopy; -samples selection; border samples; cluster; sample density;
Conference_Titel :
Computer Science and Computational Technology, 2008. ISCSCT '08. International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3746-7
DOI :
10.1109/ISCSCT.2008.132
