Machine Learning Based Prediction and Prevention of Malicious Inventory Occupied Orders

Machine Learning Based Prediction and Prevention of Malicious Inventory Occupied Orders

Qinghong Yang (School of Economics and Management, Beihang University, Beijing, China), Xiangquan Hu (School of Software, Beihang University, Beijing, China), Zhichao Cheng (School of Economics and Management, Beihang University, Beijing, China) and Kang Miao (School of Software, Beihang University, Beijing, China)
DOI: 10.4018/IJMCMC.2014100104
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Abstract

MIOOs are orders created temporarily for the purpose of occupying the inventories of sellers. MIOOs disrupt normal business activities and harm both sellers and consumers. This study aims to determine the best practice and model of the technical solutions that can effectively and systematically limit malicious inventory occupied orders (MIOOs), using the methods of analytical mining and case studies. This work contains three contributions. Firstly, this work solves MIOOs problem by using machine learning technology. The result of the study indicates that 93% of MIOOs from the sample data are actually predictable and preventable. Secondly, this work presents a methodology of solving MIOOs problem which can be applied by other companies. The methodology in this paper consists of four major steps, namely doing statistics concerning MIOOs, using logistic regression algorithm to train a mode, optimizing the model, and applying the model. Finally, this work finds unique features of MIOOs, and they can help better understanding the behind logic of MIOO producers.
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2. Literature Review

While online shopping is becoming more popular, financial fraud is becoming an increasingly serious phenomenon. Criminal activities are driven by enormous economic benefits, and they are also protected by the virtual environment. Phua et al. (2010) defined fraud as “the abuse of profit organizations system without necessarily leading to direct legal consequences.” Bhargava et al. (2003) proposed a technique that identifies auction fraud by detecting abnormal profiles and user behaviors and building patterns from exposed fraudsters in order to discover malicious intentions.

The problem of distinguishing between normal and atypical behaviors is not new and several Artificial Intelligence techniques have been employed to address it. The term classification refers to techniques (Debar et al., 1992), (Jackson et al., 1991), (Lunt et al., 1989), (Sebring et al., 1988), (Teng et al., 1990) which derive patterns of normal activities within a specific domain and then filter data into normal or exceptional categories based on the set of known patterns.

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