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A WNN-Based Approach for Network Intrusion Detection

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Intelligent Distributed Computing XIV (IDC 2021)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1026))

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Abstract

Nowadays, Intrusion Detection Systems (IDSs) are becoming more and more effective since they can benefit from the flexibility offered by Machine Learning (ML) techniques. In this work we investigate the potentiality of the Weightless Neural Networks (WNNs) as a classification method of network attacks. Traditionally, WNNs have been exploited in the image classification field and are implemented through the WiSARD algorithm. Interestingly, our analysis reveals that, applied to the IDS realm, WNNs offer surprising results in terms of performance/time complexity trade-off with respect to other ML-based techniques. The experimental assessment is carried on by considering one of the most updated datasets (CIC-IDS) in the field of the intrusion detection, where two exemplary attacks to be detected are considered: Distributed Denial of Service (DDoS) and PortScan.

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Author information

Authors and Affiliations

  1. University of Salerno, Fisciano, Italy

    Mario Di Mauro

  2. Amazon AWS, Seattle, Ireland

    Giovanni Galatro

  3. Free University of Bozen-Bolzano, Bolzano, Italy

    Antonio Liotta

Authors
  1. Mario Di Mauro
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  2. Giovanni Galatro
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  3. Antonio Liotta
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Corresponding author

Correspondence to Mario Di Mauro .

Editor information

Editors and Affiliations

  1. Computer Systems Engineering Department, Universidad Politécnica de Madrid, Madrid, Spain

    David Camacho

  2. DIIES Department, University of Reggio Calabria, Reggio Calabria, Italy

    Domenico Rosaci

  3. Department of Psychology, University of Milan Bicocca, Milan, Italy

    Giuseppe M. L. Sarné

  4. DICEAM Department, University of Reggio Calabria, Reggio Calabria, Italy

    Mario Versaci

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Di Mauro, M., Galatro, G., Liotta, A. (2022). A WNN-Based Approach for Network Intrusion Detection. In: Camacho, D., Rosaci, D., Sarné, G.M.L., Versaci, M. (eds) Intelligent Distributed Computing XIV. IDC 2021. Studies in Computational Intelligence, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-96627-0_8

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