Abstract
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 / Neurofuzzy 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 RBFSVM 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.
Highlights
Due to the sudden growth and extension of World Wide Web and network systems the computing world is witnessing enormous changes and challenges
Malicious ways like in the form of viruses, self-propagating worms, and denial of service attacks is a brutal threat to the internet and to the infrastructures using it for communication
The confusion metrics are computed for both training and testing dataset and the attained results are tabulated in table-7 and table-8
Summary
Due to the sudden growth and extension of World Wide Web and network systems the computing world is witnessing enormous changes and challenges. In order to overcome this problem, our proposed technique we come up with a solution where the number of attributes defining each of the data is reduced to a small number through a sequence of steps This process results in making the intrusion detection more efficient and yields a less complex system with a better result. In order to improve the accuracy of our previous intrusion detection technique [43], we make use of the neuro-fuzzy rather than the neural network and the linear-SVM is replaced with the radial basis-SVM The rest of this manuscript is prearranged as follows: brief review of recent researches related to our proposed technique is presented in the section 2.
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