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AutoBots: A Botnet Intrusion Detection Scheme Using Deep Autoencoders

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Proceedings of Fourth International Conference on Computing, Communications, and Cyber-Security (CCCS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 664))

Abstract

Recently, with the massive exchange of data over Internet of Things (IoT) ecosystems, attacks surfaces have also intensified. In IoT, connected devices share data over open channels and thus are highly vulnerable to security and privacy attacks. Botnet-based attacks have been found to have a significant effect on the network-based system. Thus, in this paper, we present a scheme AutoBots, which differentiates the normal and anomaly behaviour of IoT devices among the connected network. To exploit this, we consider diverse parameters like network behaviour profiles and apply autoencoders to classify and detect anomalous traffic from normal traffic. We used the BASHLITE and MIRAI IoT botnet setup and trained our network with the N-BaIoT dataset that has both benign and malicious network traffic. We compared our scheme for metrics like attack detection time, attack detection with respect to hourly traffic, deep residual accuracy, and residual loss. The presented results signify the efficacy of the proposed scheme against conventional bot-detection schemes.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, 382481, India

    Ashwin Verma, Pronaya Bhattacharya, Vivek Kumar Prasad, Rajan Datt & Sudeep Tanwar

  2. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Kolkata, West Bengal, 700135, India

    Pronaya Bhattacharya

Authors
  1. Ashwin Verma
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  2. Pronaya Bhattacharya
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  3. Vivek Kumar Prasad
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  4. Rajan Datt
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  5. Sudeep Tanwar
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Corresponding author

Correspondence to Rajan Datt .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

    Sudeep Tanwar

  2. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Slawomir T. Wierzchon

  3. Department of Computer Science and Engineering, KIET Group of Institutions, Ghaziabad, India

    Pradeep Kumar Singh

  4. Faculty of Mathematics and Informatics, Warsaw University of Technology, Warsaw, Poland

    Maria Ganzha

  5. Department of Security Engineering, University of Warwick, Coventry, UK

    Gregory Epiphaniou

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Verma, A., Bhattacharya, P., Prasad, V.K., Datt, R., Tanwar, S. (2023). AutoBots: A Botnet Intrusion Detection Scheme Using Deep Autoencoders. In: Tanwar, S., Wierzchon, S.T., Singh, P.K., Ganzha, M., Epiphaniou, G. (eds) Proceedings of Fourth International Conference on Computing, Communications, and Cyber-Security. CCCS 2022. Lecture Notes in Networks and Systems, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-99-1479-1_64

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  • DOI: https://doi.org/10.1007/978-981-99-1479-1_64

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1478-4

  • Online ISBN: 978-981-99-1479-1

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