AIDPS: Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks
AIDPS: Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks
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DOI：
10.1109/tnet.2023.3313156
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发表时间：
2023-09-20
影响因子：
3.7
通讯作者：
Sikdar，Biplab
中科院分区：
文献类型：
--
作者：
Das，Soumadeep;Pasikhani，Aryan Mohammadi;Sikdar，Biplab
水下声传感器网络（UW-ASNs）主要用于水下环境，并在许多领域得到应用。然而，缺乏安全考虑，水下环境的不稳定性和挑战性，以及用于UW-ASN的传感器节点的资源受限性（这使得它们无法采用安全原语）使得UW-ASN容易受到漏洞的影响。本文提出了一个自适应分散入侵检测和预防系统AIDPS的UW-ASN。所提出的AIDPS可以提高UW-ASN的安全性，使得它们可以有效地检测水下相关攻击（例如，黑洞，灰洞和洪水攻击）。为了确定所提出的构造的最有效的配置，我们使用几种最先进的机器学习算法（例如，自适应随机森林（ARF）、光梯度增强机和K最近邻）和概念漂移检测算法（例如，ADWIN、kdqTree和Page-Hinkley）。我们的实验结果表明，增量ARF使用ADWIN提供了最佳的性能时，实施单类支持向量机（SVM）异常检测器。此外，我们广泛的评估结果还表明，该方案优于国家的最先进的基准方法，同时提供了更广泛的理想功能，如可扩展性和复杂性。
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.