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A Comparative Study of Attribute Selection Algorithms on Intrusion Detection System in UAVs: A Case Study of UKM-IDS20 Dataset

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Risks and Security of Internet and Systems (CRiSIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13857))

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Abstract

Security issues of unmanned aerial vehicles (UAVs) have received great attention. A new dataset named UKM-IDS20 has been recently developed for intrusion detection in UAVs to distinguish between abnormal and normal behaviors. The feature selection process in datasets is essential in improving IDSs performance. Decreasing features reduces the complexity of the storage and executive load. This paper investigates the influence of feature selection IDS for UAV networks. To achieve our goal, we propose the IGC-MLP algorithm. In the beginning, the algorithm utilized feature selection algorithms to determine the optimal features. Then, the resulting features are applied to the multilayer perception classification model. We evaluate our algorithm in two scenarios (15 and 20 features). The evaluation demonstrates that our model achieves better accuracy (99.93\(\%\)). Consequently, reducing the number of features reduces memory size and CPU time needed for intrusion detection.

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Author information

Authors and Affiliations

  1. National School of Electronics and Telecommunications of Sfax, Lecturer at University of Kirkuk, Kirkuk, Iraq

    Ahmed Burhan Mohammed

  2. Digital Research Center of Sfax (CRNS); Laboratory of Signals, systeMs, aRtificial Intelligence and neTworkS (SM@RTS, Sfax University, Sfax, Tunisia

    Lamia Chaari Fourati

  3. Software department, College of Computer Science and Information Technology, University of Kirkuk, Kirkuk, Iraq

    Ahmed M. Fakhrudeen

Authors
  1. Ahmed Burhan Mohammed
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  2. Lamia Chaari Fourati
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  3. Ahmed M. Fakhrudeen
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Corresponding author

Correspondence to Ahmed Burhan Mohammed .

Editor information

Editors and Affiliations

  1. University of Sfax, Sfax, Tunisia

    Slim Kallel

  2. University of Sfax, Sfax, Tunisia

    Mohamed Jmaiel

  3. Queen's University, Kingston, ON, Canada

    Mohammad Zulkernine

  4. University of Sfax, Sfax, Tunisia

    Ahmed Hadj Kacem

  5. Polytechnique Montréal, Montréal, QC, Canada

    Frédéric Cuppens

  6. Polytechnique Montréal, Montréal, QC, Canada

    Nora Cuppens

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Mohammed, A.B., Chaari Fourati, L., Fakhrudeen, A.M. (2023). A Comparative Study of Attribute Selection Algorithms on Intrusion Detection System in UAVs: A Case Study of UKM-IDS20 Dataset. In: Kallel, S., Jmaiel, M., Zulkernine, M., Hadj Kacem, A., Cuppens, F., Cuppens, N. (eds) Risks and Security of Internet and Systems. CRiSIS 2022. Lecture Notes in Computer Science, vol 13857. Springer, Cham. https://doi.org/10.1007/978-3-031-31108-6_3

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  • DOI: https://doi.org/10.1007/978-3-031-31108-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31107-9

  • Online ISBN: 978-3-031-31108-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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