【摘要】：隨著在線交易的快速發(fā)展，在線交易欺詐已經(jīng)越來越普遍，欺詐方式從傳統(tǒng)的團伙欺詐發(fā)展成平臺欺詐。在平臺欺詐方式中，由于欺詐者廣泛的分布在網(wǎng)絡(luò)各地，他們之間并不會形成明顯的欺詐團伙，所以目前流行的團伙欺詐檢測模型并不能夠很好的發(fā)現(xiàn)平臺欺詐者。本文主要提取用戶共同交易行為特征屬性，，并結(jié)合社交網(wǎng)絡(luò)分析和用戶特征屬性，提出了針對平臺欺詐方式的檢測模型。具體研究內(nèi)容如下： 首先，通過對平臺欺詐中用戶交易行為的分析與研究，提出了反轉(zhuǎn)圖和同盟對累積交易數(shù)，得到用戶有關(guān)共同交易行為的特征屬性。對上述特征屬性進行分析，提出合理的屬性值度量方法，并且設(shè)計并行算法用于計算特征屬性值。 然后，本文選取和設(shè)計了一些用戶圖級別的重要特征屬性，反映用戶在交易圖中的誠信度和緊密度。通過對交易圖進行社交網(wǎng)絡(luò)分析來獲得這些特征屬性，考慮到交易圖中海量的用戶和交易，同樣對特征屬性值的計算設(shè)計了并行算法。文中還提出了一些用戶級別的重要特征屬性，并對這些特征屬性進行了分析。受平臺和數(shù)據(jù)集的限制，本文無法獲取全部的用戶級特征屬性。最終的特征屬性集包含了共同交易行為特征屬性、圖級別特征屬性和用戶級別特征屬性。 最后，本文設(shè)計了合理的欺詐檢測模型，基于時間特性選取最優(yōu)的數(shù)據(jù)集。針對類別不平衡分類問題和算法并行可行性問題，最終選擇隨機森林作為欺詐檢測模型的分類算法。通過對比實驗說明了選擇基于時間特性的最優(yōu)數(shù)據(jù)集和用戶共同交易行為的特征屬性能夠提高檢測性能，使用隨機森林作為分類算法能夠取得相對較優(yōu)的性能。同時，通過實驗將本文提出的檢測模型與其它模型進行了對比，本文提出的模型能夠用于對平臺欺詐用戶的檢測，同時能夠適用于真實交易平臺中類別不平衡分類問題。本文最后對模型的缺點進行了說明，并提出了可行的解決方案。
[Abstract]:With the rapid development of online transactions, online transaction fraud has become more and more common, fraud methods have developed from traditional Gang fraud to platform fraud. In the way of fraud, the fraudsters are widely distributed across the network, and they do not form obvious fraud groups, so the popular fraud detection model is now popular. It is not good to find the platform frauds. This paper mainly extracts the characteristics of the user's common transaction behavior, and combines the social network analysis and the user characteristic attributes to propose a detection model for the platform fraud mode. The specific research content is as follows:
First, through the analysis and study of the user transaction behavior in the platform fraud, the reverse graph and the alliance against the cumulative transaction number are proposed, and the characteristic attributes of the user's common transaction behavior are obtained. The characteristics of the above attributes are analyzed, the reasonable attribute value measurement method is put forward, and the parallel algorithm is designed to calculate the characteristic attribute values.
Then, this paper selects and designs some important attribute attributes of the user diagram level, reflecting the integrity and tightness of the user in the transaction diagram. Through social network analysis of the transaction graph, these characteristics are obtained. Considering the mass users and transactions in the transaction diagram, the parallel algorithm is designed for the calculation of the characteristic attribute values. In this paper, some important attribute attributes of user level are also proposed, and the characteristics are analyzed. By the restriction of the platform and data sets, this paper can not obtain all the user level feature attributes. The final feature set contains the characteristic attributes of the common transaction behavior, the attribute attributes of the graph level and the attribute attribute of the user level.
Finally, this paper designs a reasonable fraud detection model and selects the optimal data set based on the time characteristics. Aiming at the classification problem and the parallel feasibility of the algorithm, the random forest is selected as the classification algorithm of the fraud detection model. The optimal data set and the user are selected by the comparison experiment. The characteristic properties of the common transaction behavior can improve the detection performance, and use the random forest as the classification algorithm to achieve relatively superior performance. At the same time, the test model proposed in this paper is compared with other models by experiments. The proposed model can be used for the detection of the flat table fraud users, and can be applied to the reality at the same time. Finally, the shortcomings of the model are explained and feasible solutions are proposed.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.08;D924.35

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