Title :
Research on SVM Based Network Intrusion Detection Classification
Author :
Xie, Lixia ; Zhu, Dan ; Yang, Hongyu
Author_Institution :
Sch. of Comput. Sci., Civil Aviation Univ. of China, Tianjin, China
Abstract :
This paper presents a new network intrusion detection classification model based on the Support vector machine (SVM). In this model, the factor analysis (FA) algorithm converted a large number of related network behaviors features into concise integrated features, and the support vector decision function ranking method (SVDFRM) calculated the contribution of network behaviors features. Then some important network behaviors features were extracted and network behaviors were classified consequently. The experimental results show that the detection rate and the real-time of this classification model are satisfying.
Keywords :
real-time systems; security of data; support vector machines; SVM research; concise integrated features; factor analysis algorithm; network behaviors features; network intrusion detection classification; real time classification model; support vector decision function ranking method; Algorithm design and analysis; Classification algorithms; Computer science; Covariance matrix; Feature extraction; Fuzzy systems; Intrusion detection; Pattern analysis; Support vector machine classification; Support vector machines; SVM; classificaiton; feature; network intrusion detection;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3735-1
DOI :
10.1109/FSKD.2009.45