Detecting IoT Botnet Formation using Data Stream Clustering Algorithms

Gabriel Arimatéa, Admilson Ribeiro

2020

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+.

Paper Citation

in Harvard Style

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 - Volume 1: DMMLACS, ISBN 978-989-758-478-7, pages 395-402. DOI: 10.5220/0010180903950402

in Bibtex Style

@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 - Volume 1: DMMLACS,},
year={2020},
pages={395-402},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010180903950402},
isbn={978-989-758-478-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,
TI - Detecting IoT Botnet Formation using Data Stream Clustering Algorithms
SN - 978-989-758-478-7
AU - Arimatéa G.
AU - Ribeiro A.
PY - 2020
SP - 395
EP - 402
DO - 10.5220/0010180903950402