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Hierarchical Machine Learning for IoT Anomaly Detection in SDN

Amangele, Perekebode and Reed, Martin J and Al-Naday, Mays and Thomos, Nikolaos and Nowak, Mateusz (2019) Hierarchical Machine Learning for IoT Anomaly Detection in SDN. In: 2019 International Conference on Information Technologies (InfoTech), 2019-09-19 - 2019-09-20, St. Constantine and Elena resort (near the city of Varna), Bulgaria.

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The Internet of Things is a fast emerging technology, however, there have been a significant number of security challenges that have hindered its adoption. This work explores the use of machine learning methods for anomaly detection in network traffic of an IoT network that is connected through a Software Defined Network (SDN). The use of SDN allows a hierarchical approach to machine learning with the aim of reducing the packet level processing of anomaly detection at the edge through applying additional, centralized, machine learning in the SDN controller. For the sake of evaluation, we compare several supervised classification algorithms using a publicly available dataset. The results support a decision-tree based approach and show that the proposed solution promises a considerable reduction in the per-packet processing at the network edge compared to a single stage classifier.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2019 International Conference on Information Technologies (InfoTech)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 09 Oct 2019 10:56
Last Modified: 09 Oct 2019 11:15

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