Title :
Intrusion Detection Based on Support Vector Machine Divided up by Clusters
Author :
Yu-wen, Qian ; Hua-ju, Song ; Hua, Gao
Author_Institution :
Sch. of Autom., Nanjing Univ. of Sci. & Technoledge, Nanjing, China
Abstract :
In order to solve the problem that algorithm SVM (Support Vector Machine) is very slowly for intrusion detection systems, a novel algorithm based on SVM divided up by clusters was proposed. In the method, Training set is divided into many subsets by clustering algorithm, and these subsets are classified by the decision function SVM. Detection Experiments with the algorithm on intrusion detection data were completed, the results show that the method can find the intrusion actions quickly with a high precision.
Keywords :
pattern clustering; security of data; support vector machines; clustering algorithm; decision function SVM; intrusion detection systems; support vector machine; Classification algorithms; Clustering algorithms; Educational institutions; Intrusion detection; Kernel; Support vector machines; Training; Clustering; intrusion detection; network security; support Vector Machine;
Conference_Titel :
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6880-5
DOI :
10.1109/iCECE.2010.687