以模型融合為基礎之線上拍賣詐騙偵測

Online Auction Fraud Detection based on Model Fusion

陳世軒(Shih-Hsuan Chen)

指導教授 ： 張昭憲

淡江大學/商管學院/資訊管理學系碩士班/碩士(2019年)

https://doi.org/10.6846/TKU.2019.00661

摘要

隨著金流與物流等基礎建設的成熟，電子商務的蓬勃發展有目共睹，不但已成為現代人生活一部分，交易金額也年年攀高。在2017年，全球電子商務總銷售額已高達2.29兆美元，其興盛程度可見一斑。但面對如此龐大的交易金額，也引起不肖人士的覬覦，在電子商務平台中進行詐騙，而線上拍賣詐騙更佔其中的大宗。有關線上拍賣詐騙偵測，已有許多方法被提出，但對於日新月異的詐騙手法，其準確率仍有待提升。為解決此問題，本研究將配合模型融合概念，發展有效的詐騙偵測方法。首先，我們以線性迴歸組合數種傳統的分類模型，以產生更有效的融合模型，並比較傳統單一分類模型與融合模型之間的差異。之後，以不同訓練資料配比，將產生各種不同特性之模型，以多階連續過濾以及平衡過濾方式加以整合，以提升詐騙偵測的準確性。此外，由於偵測屬性集與偵測效能息息相關，本研究也探討屬性篩選對於偵測準確率之影響。為驗證提出方法之有效性，本研究採用Yahoo!奇摩實際交易資料進行實驗。與四種單一偵測模型相比較，結果顯示融合模型確實能提高偵測準確率。當使用連續過濾與平衡過濾流程時，除能獲得高準確率外，也能分段獲得較高之偵測精度。此外，結果亦顯示，使用Principle Component Analysis或Wrapper法進行屬性篩選，並無助於結果的改善。由上述結果可知，本研究提出方法確有助於改善詐騙偵測準確率，提供消費者更周全的購物安全防護機制。

關鍵字

詐騙偵測 ； 模型融合 ； 分類 ； 線上拍賣 ； 電子商務

並列摘要

With the maturity of infrastructure such as cash flow and logistics, the booming development of e-commerce is obvious to all. Not only has it become a part of modern life, but the transaction amount has also increased year by year. In 2017, global e-commerce total sales have reached 2.29 trillion US dollars, and its prosperity can be seen. However, in the face of such a large transaction amount, it also attracts a lot of fraudsters to join the e-commerce platform. Among the reported cases, online auction fraud undoubtedly forms a large proportion. There have been many methods for online auction fraud detection, but the accuracy of the ever-changing fraud scheme still needs to be improved. In order to solve this problem, this study adopts the model fusion concept to develop effective fraud detection methods to improve the accuracy of detection. First, we combine several traditional classification models with linear regression to produce a more efficient fusion model and compare the differences between the traditional single classification model and the fusion model. After that, training sets with different fraud/non-fraud ratio are used to build detection models of different characteristics. Based on these models, a multi-level continuous filtering and a balanced filtering method are developed to integrate these models and improve the accuracy of fraud detection. In addition, since the detection attribute set is closely related to the detection performance, this study also explores the impact of attribute screening on detection accuracy. In order to verify the validity of the proposed method, the study used Yahoo!Kimo actual transaction data for experiments. Compared with the four single detection models, the results show that the fusion model can improve the detection accuracy. When using continuous filtering and balanced filtering processes, in addition to high accuracy, segmentation can achieve higher detection accuracy. In addition, the results also show that feature selection does not contribute to the improvement of the results. From the above results, the proposed method does help to improve the accuracy of fraud detection and provide consumers with a more comprehensive shopping security protection mechanism.

並列關鍵字

Fraud Detection ； Model Fusion ； Classification ； Online Auctions ； e-Commerce

參考文獻

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