In the realm of e-commerce, where transactions involve multiple participants such as buyers, sellers, and intermediaries, the detection of fraudulent activities presents a significant challenge. To address this issue, our proposed method focuses on a Mult perspective approach aimed at enhancing fraud detection accuracy and efficiency. The first step involves the detection of user behaviors, wherein we leverage various techniques such as behavioral analysis and examination of transaction histories to gain insights into normal user behavior patterns. By understanding typical user interactions within the ecommerce ecosystem, we establish a baseline against which abnormal behaviors can be identified. Subsequently, we investigate into the analysis of abnor-malities for feature extraction. Utilizing sophisticated anomaly detection algorithms, we scrutinize transaction data to uncover irregular patterns indicative of potentially fraudulent activities. This process allows us to extract important features that serve as key indicators for fraud detection. Finally, we employ an ensemble classification model to implement our fraud detection mecha-nism.
bai et al. (Thu,) studied this question.