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HEDL-IDS: A Hybrid Ensemble Deep Learning Approach for Cyber Intrusion Detection

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Artificial Intelligence Applications and Innovations (AIAI 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 646))

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Abstract

The continuously increasing number of activities processed via the internet, often leaves the user vulnerable to cyber-attacks. The goal of the scientific community is to deploy innovative approaches and methodologies, capable to offer protection from potential cyber threats. This research effort aims to contribute to networks’ security by introducing the Hybrid Ensemble Deep Learning (HEDL) Intrusion Detection System (IDS) that successfully detects nine serious cyber-attacks. Its architecture comprises of three Deep Neural Networks (DNN), three Convolutional Neural Networks (CNN) and 3 Recurrent Neural Networks (RNN) using Long-Short Term Memory (LSTM) layers, running in parallel. The HEDL-IDS was successfully tested against the UNSW-NB15 dataset, achieving an overall accuracy of 98.35% and 96.25% in the training and testing phases respectively. The performance of the proposed model was evaluated by calculating Accuracy, Sensitivity, Specificity, Precision and F-1 Score. The values of all above indices were higher than 0.92, indicating the accurate performance of the developed model. The HEDL-IDS was compared with 20 robust Machine Learning Classification algorithms, sealing its reliability.

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Authors and Affiliations

  1. Department of Civil Engineering-Lab of Mathematics and Informatics (ISCE), Democritus University of Thrace, 67100, Xanthi, Greece

    Anastasios Panagiotis Psathas, Lazaros Iliadis, Antonios Papaleonidas & Dimitris Bountas

Authors
  1. Anastasios Panagiotis Psathas
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  2. Lazaros Iliadis
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  3. Antonios Papaleonidas
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  4. Dimitris Bountas
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Corresponding author

Correspondence to Anastasios Panagiotis Psathas .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Sunderland, Sunderland, UK

    John Macintyre

  4. Universidade do Minho, Guimaraes, Portugal

    Paulo Cortez

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Psathas, A.P., Iliadis, L., Papaleonidas, A., Bountas, D. (2022). HEDL-IDS: A Hybrid Ensemble Deep Learning Approach for Cyber Intrusion Detection. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 646. Springer, Cham. https://doi.org/10.1007/978-3-031-08333-4_10

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  • DOI: https://doi.org/10.1007/978-3-031-08333-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08332-7

  • Online ISBN: 978-3-031-08333-4

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