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

TL;DR

This paper introduces AIDPS, an adaptive, decentralized intrusion detection and prevention system tailored for underwater acoustic sensor networks, enhancing security against underwater attacks through machine learning and concept drift detection.

Contribution

The paper presents a novel AIDPS framework that combines machine learning and concept drift detection for improved security in resource-constrained underwater sensor networks.

Findings

Incremental ARF with ADWIN achieves optimal detection performance.

AIDPS outperforms existing benchmark methods.

System offers scalability and reduced complexity.

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.