ITrust: An Anomaly-Resilient Trust Model Based on Isolation Forest for Underwater Acoustic Sensor Networks
ITrust: An Anomaly-Resilient Trust Model Based on Isolation Forest for Underwater Acoustic Sensor Networks
复制标题
DOI：
10.1109/tmc.2020.3028369
复制
发表时间：
2022-05-01
影响因子：
7.9
通讯作者：
Martinez-Garcia, Miguel
中科院分区：
文献类型：
--
作者：
Du, Jiaxin;Han, Guangjie;Martinez-Garcia, Miguel
水声传感器网络(UASN)被广泛应用于开发各种类型的海洋应用，其中传感器节点协作完成特定的任务。由于传感器节点是无人值守的，并且不断地暴露在恶劣的环境中，关联的信任模型在节点可信性评估和缺陷节点检测中发挥着重要作用，例如网络遭受不利攻击的情况。然而，现有的信任模型只对传感器节点的通信行为和能量进行评估，而忽略了水下环境噪声对信任可靠性的影响。此外，大多数信任模型都是使用任意加权的信任度量设计的，这导致不可避免的评估错误。为了实现节点信任度的准确计算，提出了一种新的基于隔离林的异常和攻击弹性信任模型。我们将这种模式称为itrust。该模型包括两个阶段：信任度量细节和缺陷节点检测。在第一阶段，从通信信任、数据信任、能源信任和环境信任四种类型的信任度量中整合信任数据集。在第二阶段，使用隔离森林算法对获得的信任数据集进行信任评估。仿真结果表明，该信任模型能够有效地检测出缺陷节点，并在噪声环境下获得了比现有信任模型更高的检测准确率。
Underwater acoustic sensor networks (UASNs) have been widely promoted for developing various categories of marine applications, where the sensor nodes cooperate to complete specific tasks. Given the fact that the sensor nodes are unattended while continuously exposed to harsh environments, an associated trust model plays a significant role in node trustworthiness evaluation and defective node detection, such as the case of adverse attacks on the network. However, the existing trust models only evaluate the communication behavior and the energy of the sensor nodes, ignoring the effects of underwater environmental noise on trust reliability. Further, most trust models are designed with arbitraty weighted trust metrics, causing inevitable evaluation errors. To achieve the accurate calculation of node trust, we propose a new anomaly and attack resilient trust model, based on the isolation forest. We refer to this model as ITrust. The proposed ITrust model consists of two phases: trust metrics specifics and defective node detection. In the first phase, the trust dataset is integrated from four types of trust metrics: communication trust, data trust, energy trust, and environment trust. In the second stage, trust is evaluated with the obtained trust dataset using the isolation forest algorithm. Simulation results demonstrate that the proposed ITrust can detect defective nodes effectively, and achieves higher detection accuracy than that of the existing trust models in a noisy environment.