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10.1016_j.compeleceng.2023.108727.pdf (3.81 MB)

Edge intelligence for network intrusion prevention in IoT ecosystem

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journal contribution
submitted on 2024-01-18, 11:16 and posted on 2024-01-21, 05:51 authored by Mansura Habiba, Md. Rafiqul Islam, S.M. Muyeen, A.B.M. Shawkat Ali

The Internet of Things (IoT) platform allows physical devices to connect directly to the internet and upload data continuously. Insecure access makes IoT platforms vulnerable to different network intrusion attacks. As a result, the Intrusion Detection System (IDS) is a core component of a modern IoT platform. However, traditional IDS often follows rule-based detection where the rules can be changed and exposed to the attacker and becomes weak over time. An efficient IDS also needs to be dynamic and effective in real time. This paper proposes a deep learning-based algorithm to protect the network against Distributed Denial-of-Service (DDoS) attacks, insecure data flow, and similar network intrusions. A system architecture is designed for a cloud-based IoT framework to implement the proposed algorithm efficiently. The performance evaluation using standard datasets demonstrates that the proposed model provides an accuracy of up to 99.99%.

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Published in: Computers and Electrical Engineering
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Open Access funding provided by the Qatar National Library.



  • English



Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • College of Engineering - QU