Research on the Construction of Content Security Assessment Model Based on Machine Learning

With the rapid development of Internet of Things (IoT), content security issues have become increasingly prominent and become an important factor affecting the order of the network. To effectively address this challenge, it is particularly important to research the construction of content security assessment models based on machine learning (ML). In this study, we first analyze the challenges currently faced in the field of content security assessment, including the processing of massive data, complex and ever-changing threat forms, and the limitations of traditional assessment methods. On this basis, a framework for constructing a machine learning based content security assessment model is proposed, which includes key steps such as data preprocessing, feature extraction, model training, and evaluation. The aim of this study is to explore and develop an efficient ML model that assigns weights to indicators that affect content security across different accounts, comprehensively evaluates the risk index of different accounts based on actual situations, and automatically identifies and evaluates the security of network content.