Abstract: The application of wireless sensor networks (WSN) in various fields such as environmental monitoring and healthcare is gradually becoming widespread. However, sensor nodes in WSN are vulnerable to security threats, especially dishonest recommendation attacks initiated by malicious nodes, which may compromise communication integrity. Therefore, detecting malicious nodes in WSN is particularly important. In recent years, several malicious node detection approaches based on trust management were proposed to protect the WSN against dishonest recommendation attacks. However, the existing approaches ignore data consistency and reevaluation of participating nodes in trust evaluation, which seriously undermine their effectiveness. To address these limitations, we propose a hybrid malicious node detection techniquefor WSN based on the fuzzy trust model (FTM) algorithm and the Bayesian belief estimation (BBE) approach. The key idea in the proposed approach is to determine direct trust values through the FTM algorithm using the correlation of data collected over time and ascertain the trustworthiness of indirect trust values from recommendation nodes via the BBE approach. The results of simulations conducted to evaluate the effectiveness of our approach show that our model can effectively detect malicious nodes in WSN better than the previous approaches.

Key words: wireless sensor networks (WSN), malicious node detection, fuzzy trust model, Bayesian belief estimation, security threat defense

摘要： 无线传感器网络(wireless sensor networks, WSN)在环境监测、医疗保健等多个领域的应用越来越广泛.然而，WSN中的传感器节点易受安全威胁，尤其是恶意节点发起的不诚实推荐攻击，可能会破坏通信完整性.因此，对WSN中的恶意节点进行检测显得尤为重要.尽管近年来基于信任管理的恶意节点检测方法不断涌现，以增强WSN的安全性，但现有研究往往忽视了数据一致性及信任评估中对参与节点的持续评估，这在一定程度上限制了检测方法的有效性.针对这些问题，提出了一种融合了模糊信任模型(fuzzy trust model, FTM)算法和贝叶斯信念估计(Bayesian belief estimation, BBE)方法的WSN恶意节点检测新技术.其核心在于通过FTM算法考量数据随时间的关联性确定直接信任值，并通过BBE方法基于推荐节点的先验信任概率评估间接信任值的可信性.通过模拟实验对所提方法的有效性进行了验证，结果证明，该模型在检测WSN中的恶意节点方面相较于现有技术，具有更高的检测率和更低的误报率.

关键词: 无线传感器网络, 恶意节点检测, 模糊信任模型, 贝叶斯信念估计, 安全威胁防御