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Practical Application of Machine Learning based Online Intrusion Detection to Internet of Things Networks

Christohpher, Nixon, Mohamed, Sedky and Mohamed, Hassan (2019) Practical Application of Machine Learning based Online Intrusion Detection to Internet of Things Networks. Proceedings of the 2019 IEEE Global Conference on Internet of Things (GCIoT). (In Press)

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Abstract or description

Internet of Things (IoT) devices participate in an open and distributed perception layer, with vulnerability to cyber attacks becoming a key concern for data privacy and service availability. The perception layer provides a unique challenge for intrusion detection where resources are constrained and networks are distributed. An additional challenge is that IoT networks are a
continuous non-stationary data stream that, due to their variable nature, are likely to experience concept drift. This research aimed to review the practical applications of online machine learning methods for IoT network intrusion detection, to answer the question if a resource efficient architecture can be provided? An online learning architecture is introduced, with related IDS approaches reviewed and evaluated. Online learning provides a potential memory and time efficient architecture that can adapt to concept drift and perform anomaly detection, providing solutions for the resource constrained and distributed IoT perception layer.
Future research should focus on addressing class imbalance in
the data streams to ensure that minority attack classes are not
missed.

Item Type: Article
Additional Information: © 2019 IEEE
Faculty: School of Computing and Digital Technologies > Computing
Depositing User: Mohamed HASSAN
Date Deposited: 07 Nov 2019 11:27
Last Modified: 07 Nov 2019 11:27
URI: http://eprints.staffs.ac.uk/id/eprint/5943

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