Pasikhani, 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 (2022) Adversarial RL-based IDS for evolving data environment in 6LoWPAN. IEEE Transactions on Information Forensics and Security, 17. pp. 3831-3846. ISSN 1556-6013
Abstract
Low-power and Lossy Networks (LLNs) comprise nodes characterised by constrained computational power, memory, and energy resources. The LLN nodes empower ubiquitous connections amongst numerous devices (e.g. temperature, humidity, and turbidity sensors, together with motors, valves and other actuators) to sense, control and store properties of their environments. They are often deployed in hostile, unattended, and unfavourable conditions. Securing them often becomes very challenging. The extent of interconnected LLN devices poses a series of routing threats (e.g. wormhole, grayhole, DIO suppression, and increase rank attacks). Consequently, an efficient and effective intrusion detection system (IDS) is of utmost importance in identifying anomalous activities in the IPv6 over Low-powered Wireless Personal Area Networks (6LoWPAN). This article proposes a robust Adversarial Reinforcement Learning (ARL) framework to generate efficient IDSs for evolving data environments. The integration of ARL and incremental machine-learning facilitates the generation of resource-efficient and robust IDS detectors. We demonstrate in particular how such an approach, leveraging notions of 'concept drift' detection and adaptation, can handle inevitable changes in the environment, giving the IDS best chances of detecting attacks in the current profile. The range of routing attacks considered is the most comprehensive to date. For the first time, Black-box and Grey-box ML-based adversaries aiming to destabilise the 6LoWPAN are distinguished and addressed.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. |
Keywords: | Intrusion detection system; RPL attacks; 6LoWPAN; adversarial reinforcement learning; incremental machine learning; concept-drift detection |
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) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/V039156/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Feb 2024 11:06 |
Last Modified: | 16 Feb 2024 11:06 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/tifs.2022.3214099 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:209245 |