With the development of smart devices and mobile communication technologies, e-commerce has spread over all aspects of life. Abnormal transaction detection is important in e-commerce since abnormal transactions can result in large losses. Additionally, integrating data flow and control flow is important in the research of process modeling and data analysis since it plays an important role in the correctness and security of business processes. This paper proposes a novel method of detecting abnormal transactions via an integration model of data and control flows. Our model, called Extended Data Petri net (DPNE), integrates the data interaction and behavior of the whole process from the user logging into the e-commerce platform to the end of the payment, which also covers the mobile transaction process. We analyse the structure of the model, design the anomaly detection algorithm of relevant data, and illustrate the rationality and effectiveness of the whole system model. Through a case study, it is proved that each part of the system can respond well, and the system can judge each activity of every mobile transaction. Finally, the anomaly detection results are obtained by some comprehensive analysis.
Real-time probing of control-flow and data-flow in event logs
