Detecting IoT Botnet Formation using Data Stream Clustering Algorithms

Gabriel Arimatéa, Admilson Ribeiro

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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