As an important part of the new generation of information technology, the Internet of Things (IoT), which is developing rapidly, requires high user security. However, malicious nodes located in an IoT network can influence user security. Abnormal user detection and correlation probability analysis are fundamental and challenging problems. In this paper, the probabilistic model of the correlation between abnormal users (PMCAU) is proposed. First, the concept of user behavior correlation degree is proposed, which is defined as two parts: user attribute similarity degree and behavior interaction degree; the attribute similarity measurement algorithm and behavior correlation measurement algorithm are constructed, respectively, and the spontaneous and interactive behaviors of users were analyzed to determine the abnormal correlated users. Second, first-order logic grammar is used to express the before and after connection of user behavior and to deduce the probabilistic of occurrence of the correlation of behavior and determine the abnormal user groups. Experimental results show that, compared with the traditional anomaly detection algorithm and Markov logic network, this model can identify the users correlated with anomalies, make probabilistic inferences on the possible associations, and identify the potential abnormal user groups, thus achieving higher accuracy and predictability in the IoT.

中文翻译：

---

基于相关概率模型的异常用户检测

作为新一代信息技术的重要组成部分，发展迅速的物联网（IoT）要求高度的用户安全性。但是，位于IoT网络中的恶意节点可能会影响用户安全。异常的用户检测和相关概率分析是基本且具有挑战性的问题。本文提出了异常用户之间相关性的概率模型（PMCAU）。首先提出了用户行为关联度的概念，将其定义为两部分：用户属性相似度和行为交互度。分别构造了属性相似度度量算法和行为相关度量算法，并分析了用户的自发性和交互行为，确定了异常关联用户。第二，一阶逻辑语法用于表达用户行为前后的联系，并推论出行为相关性发生的概率，并确定异常用户群。实验结果表明，与传统的异常检测算法和马尔可夫逻辑网络相比，该模型可以识别与异常相关的用户，对可能的关联进行概率推断，并识别潜在的异常用户群，从而实现较高的准确性和可预测性。物联网。