User Audit Model Based on Attribute Measurement and Similarity Measurement

Abstract

The Internet of Things (IoT) is an open network. And, there are a large number of malicious nodes in the network. These malicious nodes may tamper with the correct data and pass them to other nodes. The normal nodes will use the wrong data for information dissemination due to a lack of ability to verify the correctness of the messages received, resulting in the dissemination of false information on medical, social, and other networks. Auditing user attributes and behavior information to identify malicious user nodes is an important way to secure networks. In response to the user nodes audit problem, a user audit model based on attribute measurement and similarity measurement (AM-SM-UAM) is proposed. Firstly, the user attribute measurement algorithm is constructed, using a hierarchical decision model to construct a judgment matrix to analyze user attribute data. Secondly, the blog similarity measurement algorithm is constructed, evaluating the similarity of blog posts published by different users based on the improved Levenshtein distance. Finally, a user audit model based on a security degree is built, and malicious users are defined by security thresholds. Experimental results show that this model can comprehensively analyze the attribute and behavior data of users and have more accurate and stable performance in the practical application of the network platforms.