A Complex Event Processing-Based Online Shopping User Risk Identification System

Online shopping is an important part of the development of the Internet and plays a critical role in the current and future economy. However, there are many risks in the trading process. In order to reduce the hidden risks, it is necessary to study the method of risk identification. This paper proposes user risk identification method of online shopping system based on Complex Event Process (CEP). In this paper, we use the Esper as the CEP engine and the risk behavior patterns are defined as the event pattern language. Firstly, the CEP system captures event streams by analyzing data streams in real-time. Secondly, the captured event streams are sent to the CEP’s engine. Finally, the Esper intelligently analyzes user’s online shopping risk behaviors in real-time according to the event pattern languages. User risk identification effectively guarantees the fund and account security of the shopping users.