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2021 | OriginalPaper | Buchkapitel

A Novel Approach for Detecting IoT Botnet Using Balanced Network Traffic Attributes

verfasst von : M. Shobana, Sugumaran Poonkuzhali

Erschienen in: Service-Oriented Computing – ICSOC 2020 Workshops

Verlag: Springer International Publishing

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Abstract

Over the evolution of internet technology give rise to the intelligence among tiny objects so called IoT devices. At the same time, this scenario increases the intrusion of malwares into the IoT devices e.g. Mirai, bashlite. Researchers have proposed many framework by addressing this issue. But the framework of those proposed work which are tested using Real time traffic of IoT devices is very fewer. In this work, the class imbalance problem has been identified in the BoT-IoT dataset. This problem is overcome by the random over sampling technique. Then this resultant dataset is further classified into normal and attack traffic using three machine learning classifier such as Support Vector Machine, Naive Bayes, and Decision Tree (j48) and deep learning technique such as deep neural network. The performance of the security model is evaluated using quality metrics like Precision, Recall, F-measure, Response time and ROC to identify the best classifier which is apt to detect malware in IoT devices.

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Titel: A Novel Approach for Detecting IoT Botnet Using Balanced Network Traffic Attributes
verfasst von: M. Shobana
Sugumaran Poonkuzhali
Verlag: Springer International Publishing
Buch: Service-Oriented Computing – ICSOC 2020 Workshops
Print ISBN: 978-3-030-76351-0

Electronic ISBN: 978-3-030-76352-7

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-76352-7_48

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