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.

翻译：水声传感器网络（UW-ASNs）主要应用于水下环境，并在众多领域具有广泛用途。然而，由于缺乏安全考量、水下环境的不稳定与严峻特性，以及UW-ASNs所用传感器节点资源受限（使其无法采用安全原语），UW-ASNs易受安全漏洞攻击。本文针对UW-ASNs提出了一种名为AIDPS的自适应去中心化入侵检测与防御系统。所提出的AIDPS能够提升UW-ASNs的安全性，使其高效检测水下相关攻击（例如黑洞攻击、灰洞攻击和洪泛攻击）。为确定所构建设计的最优配置，我们使用多种先进机器学习算法（如自适应随机森林、轻量梯度提升机和K近邻算法）及概念漂移检测算法（如ADWIN、kdqTree和Page-Hinkley）开展了一系列实验。实验结果表明，基于ADWIN的增量自适应随机森林结合单分类支持向量机异常检测器时，能提供最优性能。此外，我们的广泛评估结果还显示，所提出的方案优于现有最优基准方法，同时具备可扩展性与复杂度等更广泛的理想特性。