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Authors: Gabriel Arimatéa and Admilson Ribeiro

Affiliation: Federal University of Sergipe, Av. Marechal Rondon, s/n, Aracaju, Brazil

Keyword(s): Internet of Things, Botnet, Machine Learning, Security.

Abstract: The Internet of Things has gained much importance nowadays due to its applicability to many ecosystems on day-to-day use. However, these embedded systems have several hardware constraints, and theses device’s security has been neglected. Consequently, botnets malwares have taken advantage of poor security schemas on these devices. This paper proposes unsupervised machine learning using data streams to detect the botnet formation on the edge of the network. The results obtained by the algorithm includes an average of 98.43% accuracy and taking about 20.07 ms to evaluate each sample from the stream, making it reliable and fast, even in a more constrained device, such as Raspberry Pi 3 B+.

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Paper citation in several formats:
Arimatéa, G. and Ribeiro, A. (2020). Detecting IoT Botnet Formation using Data Stream Clustering Algorithms. In Proceedings of the 16th International Conference on Web Information Systems and Technologies - DMMLACS, ISBN 978-989-758-478-7; ISSN 2184-3252, pages 395-402. DOI: 10.5220/0010180903950402

@conference{dmmlacs20,
author={Gabriel Arimatéa. and Admilson Ribeiro.},
title={Detecting IoT Botnet Formation using Data Stream Clustering Algorithms},
booktitle={Proceedings of the 16th International Conference on Web Information Systems and Technologies - DMMLACS,},
year={2020},
pages={395-402},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010180903950402},
isbn={978-989-758-478-7},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Web Information Systems and Technologies - DMMLACS,
TI - Detecting IoT Botnet Formation using Data Stream Clustering Algorithms
SN - 978-989-758-478-7
IS - 2184-3252
AU - Arimatéa, G.
AU - Ribeiro, A.
PY - 2020
SP - 395
EP - 402
DO - 10.5220/0010180903950402

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