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Intelligent Intrusion Detection Algorithm Based on Multi-Attack for Edge-Assisted Internet of Things

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Security and Risk Analysis for Intelligent Edge Computing

Part of the book series: Advances in Information Security ((ADIS,volume 103))

Abstract

The security concerns surrounding edge computing have been in the forefront as the technology has become increasingly popular. There is a larger need to move computations to edge servers as more and more IoT applications that take advantage of edge computing are developed. Security at the periphery of information transmission in the Internet of Things is essential. In this work we introduce a multi-attack IDS for edge-assisted IoT that combines the back propagation (BP) Neural Network with the Radial basis function (RBF) Neural Network. Specifically, we employ a BP neural network to spot outliers and zero down on the most important characteristics for each attack methodology. A neural network based on radial basis functions (RBF) is used to spot multi-attack intrusions. The findings demonstrate great accuracy in the given multiattack scenario, demonstrating the potential and efficiency of our proposed anomaly detection methodology.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Kebri Dehar University, Kebri Dehar, Ethiopia

    S. Shitharth

  2. Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, India

    Gouse Baig Mohammed

  3. Department of Information Technology, Faculty of Engineering & Technology, Botho University, Gaborone, Botswana

    Jayaraj Ramasamy

  4. Department of Computer Science Engineering, Adhiparasakthi Engineering College, Melmaruvathur, Tamil Nadu, India

    R. Srivel

Authors
  1. S. Shitharth
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  2. Gouse Baig Mohammed
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  3. Jayaraj Ramasamy
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  4. R. Srivel
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Corresponding author

Correspondence to S. Shitharth .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, Brandon University, Brandon, MB, Canada

    Gautam Srivastava

  2. Meharry School of Applied Computer Sciences, Meharry Medical College, Nashville, TN, USA

    Uttam Ghosh

  3. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

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Shitharth, S., Mohammed, G.B., Ramasamy, J., Srivel, R. (2023). Intelligent Intrusion Detection Algorithm Based on Multi-Attack for Edge-Assisted Internet of Things. In: Srivastava, G., Ghosh, U., Lin, J.CW. (eds) Security and Risk Analysis for Intelligent Edge Computing. Advances in Information Security, vol 103. Springer, Cham. https://doi.org/10.1007/978-3-031-28150-1_6

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  • DOI: https://doi.org/10.1007/978-3-031-28150-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28149-5

  • Online ISBN: 978-3-031-28150-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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