Affiliations: [a]
The Second Affiliated Hospital of the Second Military Medical University, Shanghai City, China
| [b] Institute of Research and Development, Duy Tan University, Da Nang, Vietnam | [c] Faculty of Information Technology, Duy Tan University, Da Nang, Vietnam | [d] Department of Computer Science, Nourabad Mamasani Branch, Islamic Azad University, Mamasani, Iran
| [e] Computer Science Department, College of Computer and Information Sciences, AI Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| [f] Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, Yemen
| [g] Department of Mathematics Education, Teachers College, Can Tho University, Can Tho City, Vietnam | [h] Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Abstract: Intrusion Detection Systems (IDS) are designed to provide security into computer networks. Different classification models such as Support Vector Machine (SVM) has been successfully applied on the network data. Meanwhile, the extension or improvement of the current models using prototype selection simultaneous with their training phase is crucial due to the serious inefficacies during training (i.e. learning overhead). This paper introduces an improved model for prototype selection. Applying proposed prototype selection along with SVM classification model increases attack discovery rate. In this article, we use fuzzy rough sets theory (FRST) for prototype selection to enhance SVM in intrusion detection. Testing and evaluation of the proposed IDS have been mainly performed on NSL-KDD dataset as a refined version of KDD-CUP99. Experimentations indicate that the proposed IDS outperforms the basic and simple IDSs and modern IDSs in terms of precision, recall, and accuracy rate.
Keywords: SVM, data selection, feature selection, fuzzy rough set theory, ids
