Research Article
ML-Based Early Detection of IoT Botnets
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@INPROCEEDINGS{10.1007/978-3-030-63095-9_15, author={Ayush Kumar and Mrinalini Shridhar and Sahithya Swaminathan and Teng Joon Lim}, title={ML-Based Early Detection of IoT Botnets}, proceedings={Security and Privacy in Communication Networks. 16th EAI International Conference, SecureComm 2020, Washington, DC, USA, October 21-23, 2020, Proceedings, Part II}, proceedings_a={SECURECOMM PART 2}, year={2020}, month={12}, keywords={Internet of Things IoT Malware Mirai Botnet detection Machine Learning Anomaly detection Intrusion detection}, doi={10.1007/978-3-030-63095-9_15} }- Ayush Kumar
Mrinalini Shridhar
Sahithya Swaminathan
Teng Joon Lim
Year: 2020
ML-Based Early Detection of IoT Botnets
SECURECOMM PART 2
Springer
DOI: 10.1007/978-3-030-63095-9_15
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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