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Sanjalawe, Yousef
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Electrical & Electronics Engineering

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An efficient intrusion detection systems in fog computing using forward selection and BiLSTM Abu Zwayed, Fadi; Anbar, Mohammed; Manickam, Selvakumar; Sanjalawe, Yousef; Alrababah, Hamza; Hasbullah, Iznan H.; Almi’ani, Noor
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7143

Abstract

Intrusion detection systems (IDS) play a pivotal role in network security and anomaly detection and are significantly impacted by the feature selection (FS) process. As a significant task in machine learning and data analysis, FS is directed toward pinpointing a subset of pertinent features that primarily influence the target variable. This paper proposes an innovative approach to FS, leveraging the forward selection search algorithm with hybrid objective/fitness functions such as correlation, entropy, and variance. The approach is evaluated using the BoT-IoT and TON_IoT datasets. By employing the proposed methodology, our bidirectional long-short term memory (BiLSTM) model achieved an accuracy of 98.42% on the TON_IoT dataset and 98.7% on the BoT-IoT dataset. This superior classification accuracy underscores the efficacy of the synergized BiLSTM deep learning model and the innovative FS approach. The study accentuates the potency of the proposed hybrid approach in FS for IDS and highlights its substantial contribution to achieving high classification performance in internet of things (IoT) network traffic analysis.
Co-Authors Abu Zwayed, Fadi Almi’ani, Noor Alrababah, Hamza Anbar, Mohammed Hasbullah, Iznan H. Manickam, Selvakumar