Das, S. orcid.org/0009-0000-7756-6676, Pasikhani, A.M. orcid.org/0000-0003-3181-4026, Gope, P. orcid.org/0000-0003-2786-0273 et al. (3 more authors) (2024) AIDPS: Adaptive intrusion detection and prevention system for underwater acoustic sensor networks. IEEE/ACM Transactions on Networking. ISSN 1063-6692
Abstract
Underwater Acoustic Sensor Networks (UW-ASNs) are predominantly used for underwater environments and find applications in many areas. However, a lack of security considerations, the unstable and challenging nature of the underwater environment, and the resource-constrained nature of the sensor nodes used for UW-ASNs (which makes them incapable of adopting security primitives) make the UW-ASN prone to vulnerabilities. This paper proposes an Adaptive decentralised Intrusion Detection and Prevention System called AIDPS for UW-ASNs. The proposed AIDPS can improve the security of the UW-ASNs so that they can efficiently detect underwater-related attacks (e.g., blackhole, grayhole and flooding attacks). To determine the most effective configuration of the proposed construction, we conduct a number of experiments using several state-of-the-art machine learning algorithms (e.g., Adaptive Random Forest (ARF), light gradient-boosting machine, and K-nearest neighbours) and concept drift detection algorithms (e.g., ADWIN, kdqTree, and Page-Hinkley). Our experimental results show that incremental ARF using ADWIN provides optimal performance when implemented with One-class support vector machine (SVM) anomaly-based detectors. Furthermore, our extensive evaluation results also show that the proposed scheme outperforms state-of-the-art bench-marking methods while providing a wider range of desirable features such as scalability and complexity.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. | ||||
Keywords: | Underwater acoustic sensor networks; intrusion detection systems; 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: |
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Depositing User: | Symplectic Sheffield | ||||
Date Deposited: | 16 Feb 2024 10:59 | ||||
Last Modified: | 16 Feb 2024 10:59 | ||||
Status: | Published online | ||||
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.1109/tnet.2023.3313156 | ||||
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