Open Access

ARTICLE

A Fused Machine Learning Approach for Intrusion Detection System

by Muhammad Sajid Farooq¹, Sagheer Abbas¹, Atta-ur-Rahman², Kiran Sultan³, Muhammad Adnan Khan^4,*, Amir Mosavi^5,6,7

1 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
2 Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
3 Department of CIT, The Applied College, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
4 Department of Software, Gachon University, Seongnam, 13120, Korea
5 John von Neumann Faculty of Informatics, Obuda University, Budapest, 1034, Hungary
6 Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, 81107, Slovakia
7 Faculty of Civil Engineering, TU-Dresden, Dresden, 01062, Germany

* Corresponding Author: Muhammad Adnan Khan. Email:

Computers, Materials & Continua 2023, 74(2), 2607-2623. https://doi.org/10.32604/cmc.2023.032617

Received 23 May 2022; Accepted 24 June 2022; Issue published 31 October 2022

Abstract

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet. The interconnectivity of networks has brought various complexities in maintaining network availability, consistency, and discretion. Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities. An intrusion detection system controls the flow of network traffic with the help of computer systems. Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic. For this purpose, when the network traffic encounters known or unknown intrusions in the network, a machine-learning framework is needed to identify and/or verify network intrusion. The Intrusion detection scheme empowered with a fused machine learning technique (IDS-FMLT) is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks. The proposed IDS-FMLT system model obtained 95.18% validation accuracy and a 4.82% miss rate in intrusion detection.

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Keywords

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