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A Fused Machine Learning Approach for Intrusion Detection System

Muhammad Sajid Farooq1, Sagheer Abbas1, Atta-ur-Rahman2, Kiran Sultan3, Muhammad Adnan Khan4,*, Amir Mosavi5,6,7

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: email

Computers, Materials & Continua 2023, 74(2), 2607-2623.


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.


Cite This Article

APA Style
Farooq, M.S., Abbas, S., Atta-ur-Rahman, , Sultan, K., Khan, M.A. et al. (2023). A fused machine learning approach for intrusion detection system. Computers, Materials & Continua, 74(2), 2607-2623.
Vancouver Style
Farooq MS, Abbas S, Atta-ur-Rahman , Sultan K, Khan MA, Mosavi A. A fused machine learning approach for intrusion detection system. Comput Mater Contin. 2023;74(2):2607-2623
IEEE Style
M.S. Farooq, S. Abbas, Atta-ur-Rahman, K. Sultan, M.A. Khan, and A. Mosavi "A Fused Machine Learning Approach for Intrusion Detection System," Comput. Mater. Contin., vol. 74, no. 2, pp. 2607-2623. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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