Authors: Jamal Ghasemi; Jamal Esmaily
Addresses: Faculty of Engineering and Technology, University of Mazandaran, Babolsar, 4741613534, Iran ' Department of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, 1678815811, Iran
Abstract: Intrusion detection systems (IDSs) are able to diagnose network anomalies with the help of machine learning techniques. This paper presents a novel singular value decomposition (SVD)-based method that creates a new feature, which is applied to an IDS. The main goal is to build an effective model on datasets, which have the least possible number of features. Using the least possible number of features is inevitable in case of improving the efficiency and de-escalating the effect of curse of dimensionality in datasets with large number of features. The proposed method combines the SVD method with four classification algorithms; decision tree, Naïve Bayes, neural networks and SVM, to obtain a high accuracy in anomaly detection. This method is applied on the KDD CUP 99 and NSL_KDD datasets. Results of simulations indicate that the proposed method provides a considerable improvement in accuracy, compared with ordinary feature selection methods.
Keywords: IDSs; intrusion detection systems; machine learning; classification; SVD; singular value decomposition.
International Journal of Security and Networks, 2017 Vol.12 No.4, pp.230 - 240
Received: 05 Nov 2016
Accepted: 21 Feb 2017
Published online: 22 Nov 2017 *