A Fused Machine Learning Approach for Intrusion Detection Systemopen access
- Authors
- Farooq, Muhammad Sajid; Abbas, Sagheer; Sultan, Kiran; Atta-ur-Rahman, Muhammad Adnan; Khan, Muhammad Adnan; Mosavi, Amir
- Issue Date
- May-2023
- Publisher
- TECH SCIENCE PRESS
- Keywords
- Fused machine learning; heterogeneous network; intrusion detection
- Citation
- CMC-COMPUTERS MATERIALS & CONTINUA, v.74, no.2, pp.2607 - 2623
- Journal Title
- CMC-COMPUTERS MATERIALS & CONTINUA
- Volume
- 74
- Number
- 2
- Start Page
- 2607
- End Page
- 2623
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/87512
- DOI
- 10.32604/cmc.2023.032617
- ISSN
- 1546-2218
- Abstract
- The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet. The interconnec-tivity 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 mali-cious 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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