Abstract
In this paper, we present EDIMA, an IoT botnet detection solution to be deployed at the edge gateway installed in home networks which targets early detection of botnets. EDIMA includes a novel two-stage machine learning (ML)-based detector which first employs ML algorithms for aggregate traffic classification and subsequently Autocorrelation Function (ACF)-based tests to detect individual bots. Performance evaluation results show that EDIMA achieves high bot scanning detection accuracies with a very low false positive rate.
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Acknowledgment
This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Corporate Laboratory@University Scheme, National University of Singapore, and Singapore Telecommunications Ltd.
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Kumar, A., Shridhar, M., Swaminathan, S., Joon Lim, T. (2020). ML-Based Early Detection of IoT Botnets. In: Park, N., Sun, K., Foresti, S., Butler, K., Saxena, N. (eds) Security and Privacy in Communication Networks. SecureComm 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 336. Springer, Cham. https://doi.org/10.1007/978-3-030-63095-9_15
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DOI: https://doi.org/10.1007/978-3-030-63095-9_15
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