Intrusion detection systems using a hybrid SVD-based feature extraction method Online publication date: Wed, 22-Nov-2017
by Jamal Ghasemi; Jamal Esmaily
International Journal of Security and Networks (IJSN), Vol. 12, No. 4, 2017
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.
Online publication date: Wed, 22-Nov-2017
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Security and Networks (IJSN):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com