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Intrusion detection in networks using cuckoo search optimization

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Abstract

One of the key problems for researchers and network managers is anomaly detection in network traffic. Anomalies in network traffic might signal a network intrusion, requiring the use of a quick and dependable network intrusion detection system. Intrusion detection systems based on artificial intelligence (AI) techniques are gaining the interest of the research community as AI techniques have evolved in recent years. This research proposes a novel method for anomaly detection using artificial neural networks (ANNs) optimized using cuckoo search algorithm. For simulation purposes, the NSL-KDD dataset has been utilized with a 70:30 ratio where 70% of data is used for training and the remaining 30% is used for testing. The proposed model is then evaluated in terms of mean absolute error, mean square error, root-mean-square error, and accuracy. The results of the proposed work are compared with standard methods available in the literature including fuzzy clustering artificial neural network (FC-ANN), intrusion detection with artificial bee colony, neural network intrusion detection system, and selection of relevant feature. The results clearly show that the proposed method outperforms the listed standard methods.

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Acknowledgements

The authors would like to thank Austrian Agency for International Cooperation in Education and Research (OeAD) and Ernst Mach Follow Up Grant Program.

Funding

This research received no funding from any agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, Pakistan

    Muhammad Imran & Sangeen Khan

  2. Entertainment Computing, Faculty of Computer Science, University of Vienna, Vienna, Austria

    Helmut Hlavacs

  3. Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    Fakhri Alam Khan

  4. Computer Science, IMSciences, Peshawar, Pakistan

    Sajid Anwar

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  1. Muhammad Imran
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  2. Sangeen Khan
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  3. Helmut Hlavacs
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  4. Fakhri Alam Khan
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  5. Sajid Anwar
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Contributions

All authors contributed to the study conception and design. Data curation, methodology, and software were performed by Sangeen Khan, Sajid Anwar, and Fakhri Alam Khan. Formal analysis, investigation, validation, and writing were performed by Muhammad Imran and Helmut Hlavacs. Muhammad Imran, Sangeen Khan, and Fakhri Alam Khan contributed to the design and implementation of the research.

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Correspondence to Muhammad Imran.

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The authors declare that they have no conflict of interest.

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Additional information

Communicated by Tiancheng Yang.

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S. Khan, H. Hlavacs, F. Alam Khan, S. Anwar contributed equally to this work.

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Imran, M., Khan, S., Hlavacs, H. et al. Intrusion detection in networks using cuckoo search optimization. Soft Comput 26, 10651–10663 (2022). https://doi.org/10.1007/s00500-022-06798-2

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