近年來，線上拍賣的蓬勃發展有目共睹。線上拍賣交易兼具便利性與隱蔽性，且不受時間與空間的限制，對於交易量的提升有極大的幫助。然而，面對如此蓬勃的交易平台，許多詐騙者開始混雜其中，謀取不法利益。詐騙的方式不但多樣化，且經常隨著時間、環境改變，讓人難以防備。為了提供更安全的交易環境，本論文以行為分析為基礎，發展了一套線上拍賣詐騙早期預警方法。首先，我們針對詐騙者及正常者的交易記錄進行時序切割，再對其特徵值向量進行分群，以歸納出典型的交易者狀態。而後，針對資料集中所有的交易歷史進行狀態變遷切割，以產生與時序行為相關的分類樹偵測模型。此外，我們也利用狀態切割後的資料集，製作狀態標籤字串，並產生循序樣本庫，供使用者比對、監控可疑帳號。根據上述方法，本研究實作了一套簡單的線上拍賣交易輔助系統，讓使用者能在交易前觀察、分析交易對象的行為。為了驗證提出方法之有效性，本研究使用拍賣網站實際交易資料進行實驗。結果顯示本研究提出之方法確實有助於提升詐騙偵測之早期預警能力，並提升線上拍賣的交易安全。

In recent years, the rapid growth of online auctions were seen by everyone. The convenience, concealment and not constraints by time and space, is very helpful to raise the trading volume. However, many fraudersters start to obtain illegal benefits when facing such a vigorous trading platform. The ways of fraud are not only diverse but also changing by time and environment, difficult to avoid. In order to provide a more secure trading environment, our research development a online auction fraud early detection methods based on the analysis of behavior. First, we focus on segmentation of transaction history of fraudersters and normal users by trading events, and then proceed cluster analysis to conclude typical trader state. Second, in order to create the temporal behavior associated with the classification model we segment the transaction history by trader's state. Besides, we user the dataset that segment by trader's state to produce the state label string, and generate sequential pattern base to help the users monitor and compare the suspicious accounts. According to the methods above, our research implements a simple online auction trading decision support system. So the users can observe and analyze the behavior of account before trading. Last, to verify the effectiveness of our proposed method, we use actual transaction history on auction site to proceed experiments. The results show that the proposed method actually helps improve the early detection of auction fraud and promote the safety of online auction trading.

目錄
目錄	III
表目錄	V 
圖目錄	VI
第一章 緒論	                           1
1.1 研究背景	                           1
1.2 研究動機	                           3
1.3 研究目的	                           6
1.4 章節架構	                           7
第二章 相關技術與背景知識	                   8
2.1交易者行為變化分析	                   8
2.2群集分析(Cluster Analysis)	          10
2.3分類技術(Classification)	          10
2.4循序樣式(Sequential Pattern)	          11
2.5字串編輯距離(String Edit Distance)	  13
第三章 詐騙行為之時序分析與預警	          15
3.1 詐騙偵測模型的建立	                  15
3.2 以交易狀態變遷為基礎的詐騙偵測方法	          18
3.2.1詐騙偵測屬性集	                  18
3.2.2考慮狀態變遷的混合資料集	          19
3.2.3詐騙偵測模型的建立與使用	          24
3.3使用者狀態監控	                          24
3.3.1交易記錄轉換為狀態字串	                  24
3.3.2詐騙狀態監控與預警	                  26
第四章 系統實作	                          29
4.1 詐騙偵測與監控流程	                  29
4.2 詐騙偵測與監控系統	                  29
第五章 實驗結果	                          33
5.1實驗設定	                          33
5.2 效能驗證	                          35
第六章 結論及未來展望	                  38
參考文獻	                                  40
附錄一、實驗結果分類矩陣	                  45
附錄二、Instances-Based Learning分類實驗結果  47
附錄三、GSP訓練樣式庫	                  50

圖目錄
圖1-1 台灣Yahoo拍賣二元名聲系統 3
圖1-2 詐騙偵測流程 4
圖2-1 詐騙者生命週期 9
圖2-1 詐騙偵測決策樹 11
圖3-1 多階段交易歷史切割方法 17
圖3-2 以交易事件作為切割點 21
圖3-3 以狀態改變之交易事件作為切割點 23
圖3-4 交易事件切割點 26
圖4-1 行為監控與預警流程圖 31
圖4-2 系統操作介面-詐騙偵測 31
圖4-3 系統操作介面-狀態監控 32
圖5-1 測試集資料集合 35
圖5-2 偵測成功率(Success Rate) 37
圖5-3 詐騙偵測精度(Precision) 37
圖5-4 詐騙召回率(Recall) 37

表目錄
表2-1 範例交易資料庫(Pei & Han, 2001) 12
表3-1 交易記錄屬性 19
表3-2 詐騙者與正常者之交易行為狀態分析 22
表3-3 詐騙者分群結果 22
表3-4 一般交易者分群結果 22
表3-5 詐騙者交易記錄 26
表3-6 詐騙樣式庫與交易者Ci之相似度 28
表3-7 正常者行為樣式庫與交易者Ci之相似度 28
表5-1 分類矩陣(Confusion Matrix) 34
表5-2  SCM及WCM對不同測試集的十次平均偵測結果 36

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