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Embedding Time-Series Features into Generative Adversarial Networks for Intrusion Detection in Internet of Things Networks

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Generative Adversarial Learning: Architectures and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 217))

Abstract

In recent years, Generative Adversarial Networks (GAN) have become powerful industrial tools to facilitate various learning tasks, including anomaly detection. This chapter studies a number of GAN architectures used for anomaly detection in the data stream. Moreover, a novel approach is proposed for embedding the dynamic characteristics of the data stream into the GAN-based detector structures. In this process, a GAN model is also proposed for efficient estimation of a confidence measure during the operation that reflects how well samples can be assigned to benign data. Furthermore, this chapter designs an intrusion detection system by developing a GAN-based anomaly detector. To do this, we study the effect of the proposed approach and the selected GAN-based approaches in detecting malicious intrusions in an Internet of Things (IoT) network. Experiments are evaluated in terms of false alarm and missed alarm detection rates. The obtained results indicate the effectiveness of the proposed GAN-based detection approach for the respective task.

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Acknowledgements

This work is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant RGPIN-2021-02968.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Windsor, 401 Sunset Avenue, Windsor, N9B 3P4, Canada

    Ehsan Hallaji & Mehrdad Saif

  2. Department of Electrical and Computer Engineering and School of Computer Science, University of Windsor, 401 Sunset Avenue, Windsor, N9B 3P4, Canada

    Roozbeh Razavi-Far

Authors
  1. Ehsan Hallaji
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  2. Roozbeh Razavi-Far
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  3. Mehrdad Saif
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Corresponding author

Correspondence to Ehsan Hallaji .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering and School of Computer Science, University of Windsor, Windsor, ON, Canada

    Roozbeh Razavi-Far

  2. SeeChange.ai, Manchester, UK

    Ariel Ruiz-Garcia

  3. Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, Switzerland

    Vasile Palade

  4. The Swiss AI Lab, IDSIA, University of Lugano, USI & SUPSI, Lugano, Switzerland

    Juergen Schmidhuber

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Hallaji, E., Razavi-Far, R., Saif, M. (2022). Embedding Time-Series Features into Generative Adversarial Networks for Intrusion Detection in Internet of Things Networks. In: Razavi-Far, R., Ruiz-Garcia, A., Palade, V., Schmidhuber, J. (eds) Generative Adversarial Learning: Architectures and Applications. Intelligent Systems Reference Library, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-030-91390-8_8

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