An improved intrusion detection model based on paraconsistent logic
Fei Yan, Huanguo Zhang, Lina Wang, Min Ji Yang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
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.