淡江大學覺生紀念圖書館 (TKU Library)

系統識別號 U0002-2506201421004400
中文論文名稱 一套有效率的複合式線上拍賣詐騙偵測系統
英文論文名稱 An effective composite fraud detection system for on-line auctions
校院名稱 淡江大學
系所名稱(中) 資訊管理學系碩士班
系所名稱(英) Department of Information Management
學年度 102
學期 2
出版年 103
研究生中文姓名 林敬堯
研究生英文姓名 Ching-Yao Lin
學號 601630048
學位類別 碩士
語文別 中文
口試日期 2014-06-21
論文頁數 48頁
口試委員 指導教授-張昭憲
中文關鍵字 詐騙偵測  分類樹  線上拍賣  電子商務 
英文關鍵字 Fraud Detection  Decision Tree  Online Auction  Electronic Commerce 
中文摘要 近年來,線上拍賣的蓬勃發展有目共睹,交易量屢創新高。但在此同時,詐騙者開始進入此一便利的交易平台,利用網路的隱蔽性大肆進行不法活動。詐騙手法不斷推陳出新,甚至配合早被遺忘的陳年手法,不斷循環運用,讓有經驗的交易者也難以識破。為了識破偽裝完善的狡猾詐騙者,本研究提出了二套不同偵測方法: 分群匹配塑模法與名聲因子分類組合法,以不同的方式組合多種不同的偵測模型,以提昇詐騙偵測的準確率。在分群匹配塑模法中,訓練資料集事先以群集演算進行分群,再根據待測帳號的特性,即時建構出最合適的分類模型。名聲因子分類組合法則根據常用的拍賣者名聲模型,將分類模型分為年份、評價分數與交易類別等三種,並以協力的方式來過濾詐騙者,透過投票或權重組合來判別可疑者的身分。為了驗證方法的有效性,我們使用YAHOO!奇摩的真實交易資料進行實驗。結果顯示,本研究提出的方法能有效提升詐騙者偵測的精度,並保持優良的總體偵測成功率。
英文摘要 In recent years, the rapid growth of online auctions were seen by everyone. Trading volume hit record highs. But in the meantime, began to enter a fraudster convenient trading platform, using a network of hidden wantonly engaged in illegal activities. Scams constantly, even with long-forgotten vintage approach, continuous cycle of use , so that experienced traders also difficult to see through . Although many online auction platform precautions , but most of its design -oriented seller by the buyer cheated in order to begin to remedy the cause scammers have nothing to fear , rampant . In order to see through the disguise perfect cunning scammers , this study combined in different ways in many different classification tree , more accurate fraud detection. Type Total Year , evaluation scores and transaction types , three classification tree to generalize traders at different times of the various characteristics of different types of transactions . The classification tree is not used alone , but in a third way to filter fraudster . Combination of various ways depending on the type of classification trees to re- vote or the right to determine the combination of suspicious persons identity. In order to verify the effectiveness of the method , we use YAHOO! Kimo 's real transaction data to validate the experimental results show that our proposed method can effectively improve the accuracy of detection scammers and maintaining excellent overall detection success rate.
論文目次 目錄
第一章 前言 1
第二章 相關技術與背景知識 7
2.1 拍賣(Auctions) 7
2.2 詐騙(Fraud) 8
2.3 詐騙偵測 11
2.4 資料蒐集: 拍賣網站交易歷史記錄的下載 13
2.5 建立詐騙偵測模型 14
第三章 以多模型為基礎之詐騙偵測方法 16
3.1 分群匹配塑模法 16
3.2 名聲因子分類組合法 19
3.2.1 時間因子: 以年份作為區分 20
3.2.2 評價因子: 以總評價數作為區分 21
3.2.3 類別因子: 以商品類別作為區分 22
3.2.4 多個偵測模型的組合 23
3.3 偵測屬性集 25
第四章 實驗結果 27
4.1 實驗設定 27
4.2 效能驗證 28
參考文獻 37
附錄A 按年份塑模的加權投票 41
附錄B 按總評價數區間塑模的加權投票 43
附錄C 分群匹配塑模法實驗數據 45
附錄D 平衡式詐騙偵測驗證實驗數據 47

表3-1評價區間表 22
表3-2 屬性集 26
表4-1 指標說明表 28
表4-2 分類矩陣 28
表4-3單一分類樹模型之實驗結果 30
表4-4 分群匹配塑模法驗證 30
表4-5連續過濾法模型驗證 31
表4-6 平衡式詐騙偵測模型驗證 31
表4-7 Total及按年份塑模 32
表4-8 年份模型等值投票實驗結果(60:30) 33
表4-9 年份模型加權投票實驗結果(60:30) 33
表4-10 總評價數區間塑模驗證 34
表4-11 評價區間多模型等值投票實驗結果(60:30) 35
表4-12 附錄B具有0.944準確率的數據組(60:30) 35
表4-13 類別塑模實驗結果 35

圖2-1 連續過濾法(Chang & Chang, 2011) 12
圖2-2 互補式偵測模型(劉祐宏, 2012) 12
圖2-3下載交易歷史紀錄流程圖 14
圖2-4 決策樹範例 15
圖2-5 詐騙模型偵測流程 15
圖3-1 以最適群心塑模之流程圖 18
圖3-2 單一偵測模型 19
圖3-3 多層次詐騙偵測模型 20
圖3-4 時間因子區分為三個不同年份 21
圖3-5 多數決投票的投票流程 24
圖3-6 加權多數投票的投票流程 25
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