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VAE-Based Latent Representations Learning for Botnet Detection in IoT Networks

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Abstract

Botnets pose significant threats to cybersecurity. The infected Internet of Things (IoT) devices are used to launch unsupported malicious activities on target entities to disrupt their operations and services. To address this danger, we propose a machine learning-based method, for detecting botnets by analyzing network traffic data flow including various types of botnet attacks. Our method uses a hybrid model where a Variational AutoEncoder (VAE) is trained in an unsupervised manner to learn latent representations that describe the benign traffic data, and one-class classifier (OCC) for detecting anomaly (also called novelty detection). The main aim of this research is to learn the discriminating representations of the normal data in low dimensional latent space generated by VAE, and thus improve the predictive power of the OCC to detect malicious traffic. We have evaluated the performance of our model, and compared it against baseline models using a real network based dataset, containing popular IoT devices, and presenting a wide variety of attacks from two recent botnet families Mirai and Bashlite. Tests showed that our model can detect botnets with a satisfactory performance.

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Data Availability

The data supporting the findings of the article is available in the UC Irvine Machine Learning Repository at https://archive-beta.ics.uci.edu/ml/datasets/detection+of+iot+botnet+attacks+n+baiot.

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Authors and Affiliations

  1. Prince Laboratory, ISITCOM Hammam Sousse, University of Sousse, Sousse , Tunisia

    Ramzi Snoussi & Habib Youssef

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  1. Ramzi Snoussi
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Contributions

This paper resulted from the doctoral work of Ramzi Snoussi under the supervision of professor Habib Youssef. The original draft was written by Ramzi Snoussi then edited, corrected and approved by Habib Youssef.

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Correspondence to Ramzi Snoussi.

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Snoussi, R., Youssef, H. VAE-Based Latent Representations Learning for Botnet Detection in IoT Networks. J Netw Syst Manage 31, 4 (2023). https://doi.org/10.1007/s10922-022-09690-4

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