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

arXiv: https://arxiv.org/abs/2309.07730 · HTML: https://mlsanity.com/paper/2309.07730/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 1 of 1)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)

## Built on by (most cited first, 1 of 1)

- [Self-Adaptive Anomaly Detection with Reinforcement Learning and Human Feedback in Connected Vehicles](https://mlsanity.com/paper/2607.08373/) (2026)

## Abstract (arXiv)

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.