Author(s)

Xiang Chen

Corresponding Author

Xiang Chen

Abstract

Considering the security risks of Internet online service acquisition, it is necessary to strengthen the service dynamic reputation assessment. Based on this, this paper combines the dynamic reputation evaluation model to establish the requirements, proposes the use of machine learning to complete the support vector machine model, and enhances the dynamic evaluation of service reputation level through self-learning. Based on the introduction of model training and updating methods, it is found that the performance of the model can be obtained with high accuracy by verifying the performance of the model.

Keywords

Machine learning; dynamic reputation evaluation model; support vector machine