Pasikhan, A.M. orcid.org/0000-0003-3181-4026, Clark, J.A. orcid.org/0000-0002-9230-9739 and Gope, P. orcid.org/0000-0003-2786-0273 (2023) Incremental hybrid intrusion detection for 6LoWPAN. Computers & Security, 135. 103447.
Abstract
IPv6 over Low-powered Wireless Personal Area Networks (6LoWPAN) has grown in importance in recent years, with the Routing Protocol for Low Power and Lossy Networks (RPL) emerging as a major enabler. However, RPL can be subject to attack, with severe consequences. Most proposed IDSs have been limited to specific RPL attacks and typically assume a stationary environment. In this article, we propose the first adaptive hybrid IDS to efficiently detect and identify a wide range of RPL attacks (including DIO Suppression, Increase Rank, and Worst Parent attacks, which have been overlooked in the literature) in evolving data environments. We apply our framework to networks under various levels of node mobility and maliciousness. We experiment with several incremental machine learning (ML) approaches and various ‘concept-drift detection’ mechanisms (e.g. ADWIN, DDM, and EDDM) to determine the best underlying settings for the proposed scheme.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | 6LoWPAN; RPL; Intrusion Detection System (IDS); Increase rank attack; DIO suppression attack |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Feb 2024 11:33 |
Last Modified: | 16 Feb 2024 11:33 |
Published Version: | http://dx.doi.org/10.1016/j.cose.2023.103447 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.cose.2023.103447 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:209243 |