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Evolutionary Fuzzy Rules for Intrusion Detection in Wireless Sensor Networks

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Advances in Intelligent Networking and Collaborative Systems (INCoS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1263))

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Abstract

Next–generation digital services and applications often rely on large numbers of devices connected to a common communication backbone. The security of such massively distributed systems is a major issue and advanced methods to improve their ability to detect and counter cybernetic attacks are needed. Evolutionary algorithms can automatically evolve and optimize sophisticated intrusion detection models, suitable for different applications. In this work, a hybrid evolutionary–fuzzy classification and regression algorithm is used to evolve detectors for several types of intrusions in a wireless sensor network. The ability of genetic programming and differential evolution to construct and optimize intrusion detectors for wireless sensor networks is evaluated on a recent intrusion detection data set capturing malicious activity in a wireless sensor network.

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Acknowledgements

This work was supported by the Technology Agency of the Czech Republic in the frame of the project no. TN01000024 “National Competence Center - Cybernetics and Artificial Intelligence”, and by the projects of the Student Grant System no. SP2020/108 and SP2020/161, VSB - Technical University of Ostrava, Czech Republic.

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Authors and Affiliations

  1. VSB - Technical University of Ostrava, 17. listopadu 2172/15, Ostrava, Czech Republic

    Tarek Batiha & Pavel Krömer

Authors
  1. Tarek Batiha
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  2. Pavel Krömer
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Corresponding author

Correspondence to Tarek Batiha .

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Editors and Affiliations

  1. Department of Information and Communication Engineering Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada

    Kin Fun Li

  3. School of Science and Technology, Kwansei Gakuin University, Sanda, Japan

    Hiroyoshi Miwa

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Batiha, T., Krömer, P. (2021). Evolutionary Fuzzy Rules for Intrusion Detection in Wireless Sensor Networks. In: Barolli, L., Li, K., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2020. Advances in Intelligent Systems and Computing, vol 1263. Springer, Cham. https://doi.org/10.1007/978-3-030-57796-4_15

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