An improved intrusion detection model based on paraconsistent logic

Fei Yan; Huanguo Zhang; Lina Wang; Min Yang

doi:10.1117/12.574963

8 February 2005 An improved intrusion detection model based on paraconsistent logic

Fei Yan, Huanguo Zhang, Lina Wang, Min Yang

Proceedings Volume 5626, Network Architectures, Management, and Applications II; (2005) https://doi.org/10.1117/12.574963
Event: Asia-Pacific Optical Communications, 2004, Beijing, China

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.

Citation Download Citation

Fei Yan, Huanguo Zhang, Lina Wang, and Min Yang "An improved intrusion detection model based on paraconsistent logic", Proc. SPIE 5626, Network Architectures, Management, and Applications II, (8 February 2005); https://doi.org/10.1117/12.574963

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