AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks

• URL: http://arxiv.org/abs/2309.07730v1
• Date: Thu, 14 Sep 2023 14:07:11 GMT
• Title: AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks
• Authors: Soumadeep Das, Aryan Mohammadi Pasikhani, Prosanta Gope, John A. Clark, Chintan Patel and Biplab Sikdar
• Abstract summary: This paper proposes an Adaptive decentralised Intrusion Detection and Prevention System (AIDPS) for Underwater Acoustic Sensor Networks (UW-ASNs) The proposed AIDPS can improve the security of the UW-ASNs so that they can efficiently detect underwater-related attacks.
• Score: 15.322411959318929
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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