| Summary: | 博士 === 淡江大學 === 管理科學學系博士班 === 101 === In recent years, online auction has become one of the most successful business models; however, the tremendous profit also appeals to many fraudsters. Schemed fraudsters camouflage their malicious intent to distract customers for profit, seriously threatening online auction security. This dissertation aims to develop a set of methods for constructing an effective early fraud detection system. This research proposes various detection methods taking detection cost into account to enhance the practicality of such a system, including the following:
(1) To satisfy the need of early fraud detection, a phased profiling approach partitions the transaction histories of traders before detection model construction. The latent behavior of uncovered fraudsters can be extracted from these segmented transaction histories presenting different periods of lifespan that is helpful in observing fraudulent behavior fluctuation.
(2) To address the diversity of latent behavior, a hybrid phased modeling method increases the detection accuracy for latent fraudsters. This method extracts features from different phases of the latency period to construct models for enhancing the capability of early fraud detection. To further improve accuracy, a two-stage detection procedure uses various detection models to carefully examine the behavior of a suspicious account.
(3) To reduce detection costs, a modified wrapper approach is used to select a concise set of measured attributes, which is then used to construct the model. In addition, a complement phased modeling method increases the accuracy while facilitating the data downloading from the auction site, providing a cost-effective detection procedure.
(4) To analyze the evolution of fraudulent behavior, clustering methods incorporated with phased profiling are used to classify the types of fraudsters. This analysis helps to parse fraudulent behavior with greater granularity and resolution.
To test the effectiveness of the methods we proposed, real transaction records were collected from Yahoo!Taiwan. The proposed methods not only improve the accuracy of fraud detection but can also identify latent fraudsters, a necessary requirement for early detection. The results show that these methods improve the practicality of fraud detection system, allowing online auction participants and the trading environment to be secured in a cost-effective way.
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