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A hybrid heuristics artificial intelligence feature selection for intrusion detection classifiers in cloud of things

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Abstract

Cloud computing environments provide users with Internet-based services and one of their main challenges is security issues. Hence, using Intrusion Detection Systems (IDSs) as a defensive strategy in such environments is essential. Multiple parameters are used to evaluate the IDSs, the most important aspect of which is the feature selection method used for classifying the malicious and legitimate activities. We have organized this research to determine an effective feature selection method to increase the accuracy of the classifiers in detecting intrusion. A Hybrid Ant-Bee Colony Optimization (HABCO) method is proposed to convert the feature selection problem into an optimization problem. We examined the accuracy of HABCO with BHSVM, IDSML, DLIDS, HCRNNIDS, SVMTHIDS, ANNIDS, and GAPSAIDS. It is shown that HABCO has a higher accuracy compared with the mentioned methods.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China (2020YFB1406902), the Key-Area Research and Development Program of Guangdong Province (2020B0101360001), the Shenzhen Science and Technology Research and Development Foundation (JCYJ20190806143418198), the National Natural Science Foundation of China (NSFC) (61872110), and the Peng Cheng Laboratory Project (PCL2021A02).

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Arun Kumar Sangaiah

  2. Department of Computer Science and Technology (Cyberspace Security), Harbin Institute of Technology, Shenzhen, China

    Amir Javadpour & Weizhe Zhang

  3. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Forough Ja’fari

  4. ADiT-Lab, Electrotechnics and Telecommunications Department, Instituto Politécnico de Viana do Castelo, Porto, 4900-347, Portugal

    Amir Javadpour & Pedro Pinto

  5. School of Computing Science and Engineering, VIT University, Vellore, India

    Arun Kumar Sangaiah & Sudha Balasubramanian

  6. Universidade da Maia, Maia, 4475-690, Portugal

    Pedro Pinto

  7. INESC TEC, Porto, 4200-465, Portugal

    Pedro Pinto

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  1. Arun Kumar Sangaiah
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  2. Amir Javadpour
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  3. Forough Ja’fari
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  4. Pedro Pinto
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  5. Weizhe Zhang
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  6. Sudha Balasubramanian
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Correspondence to Amir Javadpour or Weizhe Zhang.

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Sangaiah, A.K., Javadpour, A., Ja’fari, F. et al. A hybrid heuristics artificial intelligence feature selection for intrusion detection classifiers in cloud of things. Cluster Comput 26, 599–612 (2023). https://doi.org/10.1007/s10586-022-03629-9

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  • DOI: https://doi.org/10.1007/s10586-022-03629-9

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