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系統識別號 U0002-2506201202364100
中文論文名稱 線上拍賣詐騙偵測之屬性挑選與流程設計
英文論文名稱 Construction for the Classification Feature Selection and the Fraud Detection Flow in Online 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 100
學期 2
出版年 101
研究生中文姓名 劉祐宏
研究生英文姓名 Yu-Hung Liu
學號 699630918
學位類別 碩士
語文別 中文
口試日期 2012-05-26
論文頁數 69頁
口試委員 指導教授-張昭憲
委員-楊欣哲
委員-戚玉樑
委員-廖賀田
中文關鍵字 詐騙偵測  屬性挑選  分類樹  線上拍賣  電子商務 
英文關鍵字 Fraud Detection  Attribute Selection  Decision Tree  Online Auction  Electronic Commerce 
學科別分類 學科別社會科學管理學
學科別社會科學資訊科學
中文摘要 隨著線上拍賣交易量的快速成長,陸續發生許多交易糾紛,其中最嚴重的莫過於詐騙。拍賣平台提供的二元名聲系統不足以保護消費者避開陷阱,有時更淪為詐騙者的行騙工具。有鑑於此,學者們紛紛提出各種詐騙偵測方法,期能協助使用者避開詐騙、安心交易。典型的做法為設計一套詐騙偵測屬性集,並使用不同的學習演算法來塑模。這些方法雖然各有特色,但很少能兼顧偵測的成本與效益,此外,相關研究多使用單一偵測模型,使其效能受到限制。為了節省偵測成本,並提升偵測準確性,本研究首先發展一套詐騙偵測屬性篩選演算法-EFCBF,期能以較少的屬性,獲得較佳的偵測結果,以降低偵測成本。根據挑選的屬性集,本研究進一步提出一套平衡式詐騙偵測流程,以互補方式結合多個偵測模型,提升總體的準確性。為了驗證提出方法的有效性,我們蒐集了Yahoo!Taiwan的交易資料進行實驗,並與前人研究比較。結果顯示EFCBF屬性挑選方法與平衡式詐騙偵測流程均能以較少的成本,提供較佳的詐騙偵測結果。最後,為了增進實用性,本研究根據上述方法,開發了一套線上拍賣決策支援工具-AuctionGuard,協助使用者挑選交易對象,及時避開詐騙與交易糾紛,提升交易的滿意度。
英文摘要 With the rapid growth of online auction transaction, there have been incidents of many trade disputes, the most serious of which is fraud. The binary reputation system in online auction platform is insufficient to protect consumers avoid the trap, sometimes even become a defraud tool used by fraudsters. For this reason, scholars have proposed a variety of fraud detection methods to help users to avoid fraud to help ease transactions. The typical approach for detect fraud is the design of a fraud detection attribute set, and use different learning algorithms to build fraud detection models. Although these methods have their own characteristics, but few take into account the costs and benefits of detection, in addition, related studies using a single detection model and so that performance is limited. In order to save the detection cost and improve the detection accuracy, this research have developed a fraud detection attribute filtering algorithm-EFCBF, using fewer attributes but gain better detection results to reduce the detection cost. According to the set of selected attributes, we further proposed a balanced fraud detection process, the combination of multiple detection models in a complementary manner to enhance the overall accuracy. In order to verify the proposed method, we experiment by using the collected transaction information in Yahoo! Taiwan, and compared with the previous studies. The results show that EFCBF and balanced fraud detection process both can provide better fraud detection results at less cost. Finally, in order to enhance the practicality of this research under this approach, we developed a set of online auction decision support tools-AuctionGuard, to assist the user in the selection of trading partners in a timely manner to avoid fraud and trading disputes for improve satisfaction with the transaction.
論文目次 目錄
1. 緒論 1
2. 相關技術與概念介紹 5
2.1 詐騙偵測屬性 5
2.2 屬性篩選方法 10
2.3 詐騙偵測模型 13
2.3.1 偵測模型的建立 13
2.3.2 多階段詐騙偵測流程 16
3. 詐騙屬性篩選及模型建構 18
3.1 EFCBF(Enhanced FCBF)篩選方法 18
3.2 平衡式詐騙偵測流程 22
4. 系統實作 26
4.1 詐騙偵測 27
4.2 名聲評估模組 29
4.2.1 趨勢分析 30
4.2.2 綜合分析 37
5. 實驗結果 40
5.1 實驗設定 40
5.2 屬性選取演算法之效能驗證 42
5.2.1 EFCBF之效能驗證 42
5.2.2 少量資料狀況下之EFCBF效能驗證 46
5.3 平衡式詐騙偵測流程效能驗證 50
5.4 新資料詐騙偵測效能驗證 52
6. 結論及未來展望 54
參考文獻 56
附錄一、FCBF 10次實驗所挑選之指標集 61
附錄二、EFCBF 10次實驗所挑選之指標集 63
附錄三、實驗結果匯總比較 69


表目錄
表 2 1:Chau等人所提出之屬性集(Chau et al., 2006) 7
表 2 2:Chang & Chang提出的屬性集(Chang & Chang, 2009) 8
表 2 3:鄭孝儒所提出的詐騙偵測屬性集(鄭孝儒, 2011) 9
表 2 4:電腦公司顧客資料數據表 14
表 5 1:交易資料年份分布 41
表 5 2:Confusion Matrix 41
表 5 3:10次實驗中FCBF曾經挑出過的屬性 43
表 5 4:10次實驗中EFCBF曾經挑出過的屬性 43
表 5 5:不同屬性集詐騙偵測比較 45
表 5 6:爆發前90天詐騙偵測實驗結果 47
表 5 7:爆發前60天詐騙偵測實驗結果 48
表 5 8:爆發前30天詐騙偵測實驗結果 49
表 5 9:不同資料切割方式下之偵測成功率比較 50
表 5 10:平衡式詐騙偵測模型實驗與各項實驗比較 51
表 5 11:新加入詐騙者發生年份分布 53
表 5 12:新加入資料後詐騙偵測實驗結果 53


圖目錄
圖 2 1:FCBF演算法之虛擬碼(Yu et al., 2004) 13
圖 2 2:鄭孝儒(2011)所提出的二階段詐騙偵測模型 17
圖 3 1:EFCBF之虛擬碼 21
圖 3 2:平衡式詐騙偵測模型架構圖 25
圖 4 1:系統架構圖 27
圖 4 2:詐騙偵測工具,偵測為正常賣家 28
圖 4 3:詐騙偵測工具,偵測為詐騙 29
圖 4 4:賣家之交易量、交易金額與平均金額走勢圖 32
圖 4 5:拍賣趨勢分析─交易量MA7 35
圖 4 6:拍賣趨勢分析─交易量MA30 35
圖 4 7:拍賣趨勢分析─平均金額MA7 36
圖 4 8:拍賣趨勢分析─年度交易分布圓餅圖 36
圖 4 9:賣家基本資料介面 39
參考文獻 參考文獻
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