Online E-Commerce Fraud: A Large-Scale Detection and Analysis
| Autor: | Tianyu Du, Haifeng Lu, Chen Chu, Shouling Ji, Qinming He, Haiqin Weng, Zhao Li |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Service (systems architecture)
Data processing Computer science business.industry media_common.quotation_subject 02 engineering and technology E-commerce Computer security computer.software_genre Open data Promotion (rank) 020204 information systems Scale (social sciences) Scalability 0202 electrical engineering electronic engineering information engineering Task analysis ComputingMilieux_COMPUTERSANDSOCIETY 020201 artificial intelligence & image processing business computer media_common |
| Zdroj: | ICDE |
| Popis: | Nowadays, e-commerce has become prevalent world-wide. With the big success of e-commerce, many malicious promotion services also rise: with the goal of increasing sales, malicious merchants attempt to promote their target items by illegally optimizing the search results using fake visits, purchases, etc. In this paper, we study the fraud detection problem on large-scale e-commerce platforms. First, we develop an efficient and scalable AnTi-Fraud system (ATF) to detect e-commerce frauds for large-scale e-commerce platforms, and implement it in parallel on a large-scale computing platform, called Open Data Processing Service (ODPS). Then, we evaluate ATF using two real large-scale e-commerce datasets (with tens of millions users and items). The results demonstrate that both the precision and the recall of ATF can achieve 0.97+, which suggests that ATF is very effective. More importantly, we deploy ATF on the Taobao platform of Alibaba, which is one of the world's largest e-commerce platforms. The evaluation results show that ATF can also achieve an accuracy of 98.16% on Taobao, which again suggests that ATF is very effective and deployable in practice. Our study in this paper is expected to shed light on defending against online frauds for practical e-commerce platforms. |
| Databáze: | OpenAIRE |
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