An intrusion detection based on support vector machines with a voting weight schema

Authors:
Rung-Ching Chen;Su-Ping Chen
Affiliations:
Department of Information Management, Chaoyang University of Technology, Wufong Township, Taichung County, Taiwan, R.O.C.;Department of Information Management, Chaoyang University of Technology, Wufong Township, Taichung County, Taiwan, R.O.C.
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

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Abstract

Though IDS (Intrusion Detection System) have been used for many years, the large number of returned alert messages leads to management inefficiencies. In this paper, we propose a novel method based on SVM (Support Vector Machines) with a voting weight schema to detect intrusion. First, TF (Term Frequency), TF-IDF (Term Frequency-Inverse Document Frequency) and entropy features are extracted from processes. Next, these three features are sent to the SVM model for learning and then for testing. We then use a general voting schema and a voting weight schema to test attack detection rate, false positive rate and accuracy. Preliminary results show the SVM with a voting weight schema combines low the false positive rates and high accuracy.