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Metaheuristic Based IDS Using Multi-objective Wrapper Feature Selection and Neural Network Classification

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Advances in Cyber Security (ACeS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1347))

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Abstract

Due to the significant ongoing expansion of computer networks in our lives nowadays, the demand for network security and protection from cyber-attacks has never been more imperative to either clients or businesses alike, which signifies the key role of cyber intrusion detection systems in network security. This article proposes a cyber-intrusion detecting system classification with MLP trained by a hybrid metaheuristic algorithm and feature selection based on multi-objective wrapper method. The classifier, named as HADMLP is trained using a hybridization of the artificial bee colony along with the dragonfly algorithm. A multi-objective artificial bee colony model which is wrapper-based is used for selection of feature. Hence, collective name of the proposed technique referred as MO-HADMLP. For performance evaluation, the proposed method was assessed using ISCX 2012 and KDD CUP 99 datasets. The results of our experiments indicate a significant enhancement to the efficacy of network intrusion detection when compared to other approaches.

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Author information

Authors and Affiliations

  1. Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Terengganu, Malaysia

    Waheed Ali H. M. Ghanem

  2. Faculty of Engineering and Faculty of Education-Aden and Saber, Aden University, Aden, Yemen

    Waheed Ali H. M. Ghanem

  3. Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Kuala Terengganu, Malaysia

    Yousef A. Baker El-Ebiary

  4. School of Computing and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia

    Mohamed Abdulnab & Mohammad Tubishat

  5. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Nayef A. M. Alduais

  6. Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Pekan, Malaysia

    Abdullah B. Nasser

  7. National Advanced IPv6 Center, Universiti Sains Malaysia, Gelugor, Malaysia

    Nibras Abdullah

  8. Faculty of Computer Science and Engineering, Hodeidah University, Al Hudaydah, Yemen

    Nibras Abdullah & Ola A. Al-wesabi

Authors
  1. Waheed Ali H. M. Ghanem
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  2. Yousef A. Baker El-Ebiary
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  3. Mohamed Abdulnab
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  4. Mohammad Tubishat
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  5. Nayef A. M. Alduais
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  6. Abdullah B. Nasser
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  7. Nibras Abdullah
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  8. Ola A. Al-wesabi
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Corresponding author

Correspondence to Waheed Ali H. M. Ghanem .

Editor information

Editors and Affiliations

  1. National Advanced IPv6 Centre, Universiti Sains Malaysia, Penang, Malaysia

    Mohammed Anbar

  2. Hodeidah University, Hodeidah, Yemen

    Nibras Abdullah

  3. National Advanced IPv6 Centre, Universiti Sains Malaysia, Penang, Malaysia

    Selvakumar Manickam

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Ghanem, W.A.H.M. et al. (2021). Metaheuristic Based IDS Using Multi-objective Wrapper Feature Selection and Neural Network Classification. In: Anbar, M., Abdullah, N., Manickam, S. (eds) Advances in Cyber Security. ACeS 2020. Communications in Computer and Information Science, vol 1347. Springer, Singapore. https://doi.org/10.1007/978-981-33-6835-4_26

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  • DOI: https://doi.org/10.1007/978-981-33-6835-4_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6834-7

  • Online ISBN: 978-981-33-6835-4

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