期刊名称：International Journal of Network Security & Its Applications
印刷版ISSN：0975-2307
电子版ISSN：0974-9330
出版年度：2013
卷号：5
期号：1
DOI：10.5121/ijnsa.2013.510671
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal activities happening in a computer system. In recent years different soft computing based techniques have been proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfect technology. This has provided an opportunity for data mining to make quite a lot of important contributions in the field of intrusion detection. In this paper we have proposed a new hybrid technique by utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neuro-fuzzy and radial basis function(RBF) SVM for fortification of the intrusion detection system. The proposed technique has five major steps in which, first step is to perform the relevance analysis, and then input data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that each of the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster. Subsequently, a vector for SVM classification is formed and in the last step, classification using RBF- SVM is performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 dataset and we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Our technique could achieve better accuracy for all types of intrusions. The results of proposed technique are compared with the other existing techniques. These comparisons proved the effectiveness of our technique.
