Title :
A Fuzzy Clustering Approach for Intrusion Detection
Author :
Zeng, QingPeng ; Wu, ShuiXiu
Author_Institution :
Sch. of Inf. Eng., NanChang Univ., NanChang, China
Abstract :
Detection of intrusion attacks is an important issue in network security, now fuzzy set theory has been applied to many fields, therefore, research into fuzzy clustering method for knowledge is significant not only to theory, but also to application. the Fuzzy Possibility C-Means Algorithm for intrusion detection is adopted in this paper, the experiments with KDD Cup 1999 data demonstrate that our proposed method achieves 91.00% average detection rate, and the false positive rate ranges from 0.50% to 1.80%, the total performance evaluation is outperforms the RIPPER method.
Keywords :
fuzzy set theory; pattern clustering; possibility theory; security of data; fuzzy clustering; fuzzy possibility C-means algorithm; fuzzy set theory; intrusion attack detection; network security; Clustering algorithms; Computer networks; Computer security; Data engineering; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Information security; Intrusion detection; Knowledge engineering; Fuzzy Clustering; Fuzzy Possibility C-Means Algorithm; Intrusion Detection; RIPPER;
Conference_Titel :
Web Information Systems and Mining, 2009. WISM 2009. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3817-4
DOI :
10.1109/WISM.2009.150
