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Proceedings Paper

An improved intrusion detection model based on paraconsistent logic
Author(s): Fei Yan; Huanguo Zhang; Lina Wang; Min Yang
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Paper Abstract

A major difficulty of current intrusion detection model is the attack set cannot be separated from normal set thoroughly. On the basis of paraconsistent logic, an improved intrusion detection model is proposed to solve this problem. We give a proof that the detection model is trivial and discuss the reason of false alerts. A parallel paraconsistent detection algorithm is presented to develop the detection technology based on our model. An experiment using network connection data, which is usually used to evaluate the intrusion detection methods, is given to illustrate the performance of this model. We use one-class supported vector machine (SVM) to train our profiles and use supported vector-clustering (SVC) algorithm to update our detection profiles. Results of the experiment indicate that the detection system based on our model can deal with the uncertain events and reduce the false alerts.

Paper Details

Date Published: 8 February 2005
PDF: 10 pages
Proc. SPIE 5626, Network Architectures, Management, and Applications II, (8 February 2005); doi: 10.1117/12.574963
Show Author Affiliations
Fei Yan, Wuhan Univ. (China)
Huanguo Zhang, Wuhan Univ. (China)
Lina Wang, Wuhan Univ. (China)
Min Yang, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 5626:
Network Architectures, Management, and Applications II
S. J. Ben Yoo; Gee-Kung Chang; Guangcheng Li; Kwok-wai Cheung, Editor(s)

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