A Novel SHiP Vector Machine for Network Intrusion Detection

Title

A Novel SHiP Vector Machine for Network Intrusion Detection

Creator

Sowmya, T.; Anita, E. A. Mary

Description

In this paper, network intrusion detection is proposed using an improved version of the support vector machine model to detect DoS attacks. Here, the SVM model considers the weight parameter along with the kernel to find the best decision boundary that separates the data into DoS and normal. The proposed model provides a novel kernel trick that reduces the overlapping of data. The intrusion detection system aims to construct an ideal system that can detect attacks with very high performance using a ShiP vector machine(Sophisticated High Performance Vector Machine). The framework comprises three major steps: data collection and preprocessing, Recursive Feature Elimination (RFE) based feature selection, and the ShiP Vector Machine classification strategy. The system is evaluated using the DoS dataset from UNSWNB15 and real time PSD-23 sniffer dataset. DoS data is generated by extracting the normal and DoS attacks from the UNSWNB15 dataset. Experimental results show that the proposed ShiP vector machine shows outstanding performance by achieving 96.44 % accuracy on the DoS dataset and 90.12 % accuracy for real time PSD-23 data. 2013 IEEE.

Source

IEEE Access;Volume;13;pp.117445-117463

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

DoS dataset recursive feature elimination (RFE); PSD-23 dataset; SHiP vector machine (sophisticated high performance vector machine)

Coverage

Sowmya T., Manipal Academy of Higher Education, Manipal Institute of Technology, Manipal, Bengaluru, 576104, India, Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, 560074, India; Anita E.A.M., Christ University, Department of Computer Science and Engineering, Karnataka, Bengaluru, 560074, India

Rights

All Open Access; Gold Open Access; Green Open Access

Relation

ISSN: 21693536;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.1109/ACCESS.2025.3586296

https://www.scopus.com/pages/publications/105010190732?origin=resultslist