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Botnet Attack Detection Approach in IoT Networks

Botnet Attack Detection Approach in IoT Networks —An approach to detecting network attacks based on deep learning (autoencoders) is proposed. It is shown that learning examples can be obtained by connecting IoT devices to the network, as long as the traffic does not carry malicious code. Statistical values and functions extracted from traffic are proposed; patterns of IoT devices are based on them. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

Botnet Attack Detection Approach in IoT Networks

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Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2022. ISSN 0146-4116, Automatic Control and Computer Sciences, 2022, Vol. 56, No. 8, pp. 838–846. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Problemy Informatsionnoi Bezopasnosti, Komp’yuternye Sistemy.
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/s0146411622080259
Publisher site
See Article on Publisher Site

Abstract

—An approach to detecting network attacks based on deep learning (autoencoders) is proposed. It is shown that learning examples can be obtained by connecting IoT devices to the network, as long as the traffic does not carry malicious code. Statistical values and functions extracted from traffic are proposed; patterns of IoT devices are based on them.

Journal

Automatic Control and Computer Sciences – Springer Journals

Published: Dec 1, 2022

Keywords: Internet of things; network attack; intrusion detection system; autoencoder; principal component analysis; unsupervised learning

References

  • A review of anomaly based intrusion detection systems
    Jyothsna, V.; Prasad, V.V.R.; Prasad, K.M.
  • Effective anomaly intrusion detection system based on neural network with indicator variable and rough set reduction
    Sadek, R.A.; Soliman, M.S.; Elsayed, H.S.
  • N-BaIoT: Network-based detection of IoT botnet attacks using deep autoencoders
    Meidan, Ya.; Bohadana, M.; Mathov, Ya.; Mirsky, Yi.; Shabtai, A.; Breitenbacher, D.; Elovici, Yu.
  • Cybersecurity risk assessment in smart city infrastructures
    Kalinin, M.; Krundyshev, V.; Zegzhda, P.