CATS: Cross-Platform E-Commerce Fraud Detection

Nowadays, the popularity of e-commerce has brought huge economic benefits to factories, third-party merchants, and e-commerce service providers. Driven by such huge economic benefits, malicious merchants attempt to promote items through inserting fraudulent purchases, fake review scores, and/or feedback, into them. Mitigating this threat is challenging due to the difficulty of obtaining internal e-commerce data, the variance of e-commerce services used by malicious merchants, and the reluctance of service providers in cooperation. In this paper, we present an efficient, platform-independent, and robust e-commerce fraud detection system, CATS, to detect frauds for different large-scale e-commerce platforms. We implement the design of CATS into a prototype system and evaluate this prototype on the world's popular e-commerce platform Taobao. The evaluation result on Taobao shows that CATS can achieve a high accuracy of 91% in detecting frauds. Based on this success, we then apply CATS on another large-scale e-commerce platforms, and again CATS achieves an accuracy of 96%, suggesting that CATS is very effective on real e-commerce platforms. Based on the cross-platform evaluation results, we conduct a comprehensive analysis on the reported frauds and reveal several abnormal yet interesting behaviors of those reported frauds. Our study in this paper is expected to shed light on defending against frauds for various e-commerce platforms.