結合社會網路分析與網絡結構探勘偵測網路拍賣哄抬評價之共犯群體

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姓名

鄭義穎(Yi- ying Jheng) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

結合社會網路分析與網絡結構探勘偵測網路拍賣哄抬評價之共犯群體
(Combing Social Network Analysis with Web Structure Mining for detecting collusive fraud group in online auction)

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摘要(中)

隨著網路拍賣的普及，網路拍賣詐欺也逐漸變成犯罪的手法，其常見的手法便是不肖賣家透過拍賣網站所具有高度匿名與進入門檻低的特性，進行帳號與帳號之間相互哄抬評價，以創造高評價分數的假像來引誘買家，此種情形屢見不鮮。但之前大多數的研究僅利用社會網路分析（Social Network Analysis）來偵測哄抬評價之共犯群體，大都不能完整的偵測出整體詐欺群體的關係且非線上即時偵測。
因此，本研究針對此種詐欺共犯群體，提出一個模組化的詐欺共犯群體偵測流程，以彌補網路拍賣評價系統之不足。首先，我們利用K-Core分群演算法的概念來設計我們代理人搜尋路徑以達成可完整與立即地找出潛在的共犯群體；第二，透過本研究定義的資料前處理動作，進行資料清理；第三，使用PageRank演算法找出在群體中具有權威性與重要性的帳號，並計算其有效的指標；第四，使用Auction Fraud Rank演算法計算第二個有效的指標，此演算法是修改自PageRank演算法其目的是為了能夠讓演算法同時考量網絡結構性與帳號本身潛在的危險度；最後，我們使用調適性類神經推論系統(Adaptive-Network-based Fuzzy Inference System, ANFIS)結合社會網路分析與網路結構探勘(Web Structure Mining)來偵測群體中每個帳號的危險性。本研究使用真實案例的方式來檢驗所提出的系統架構是否可以有效幫助使用者找出潛在的共犯群體。

摘要(英)

With the popularity of the online purchase, online auction fraud has become a kind of criminal in our daily life. The most common fraud method is that auctioneers use the characteristics of high anonymity and the lower thresholds of the e-Auctions to create multiple accounts and manipulate their reputations. In this way, they can deceive the buyer by their high reputations. But most of the previous researches focus on only using the Social Network Analysis to detect the inflated reputation behaviors of the auction fraud group. Thus, it can’t detect the relationship of whole group fraudsters and the execution process should not be Online real-time.
Therefore, the research proposes a new process which can detect collusive group: First, we use the concept of k-core clustering algorithm to design our searching path for Agent in order to capture the potential collusive group completely and immediately. Second, we define a data preprocessing to clean-up unrelated data. Third, we use the PageRank algorithm to discover authoritative and important accounts in the group and calculate the useful indicator. Four, Auction Fraud Rank algorithm, an extension to the standard PageRank algorithm, takes into account the importance of both Web structure and the potential risk of account in order to calculate the second useful indicator. Finally, we use ANFIS to combine SNA and WSN to detect the risk of each account in the group. In the research, we use real cases to validate whether the proposed system can effectively help auctioneers to find the potential collusive group.

關鍵字(中)

★ 調適性類神經模糊推論系統
★ 社會網路分析
★ 線上拍賣
★ 網絡結構探勘
★ 詐欺共犯群體
★ PageRank演算法

關鍵字(英)

★ PageRank Algorithm
★ Fraud Group Detection
★ K-Core
★ Social Network Analysis
★ Online Auction
★ Web Structure Mining
★ ANFIS

論文目次

目錄
摘要 i
Abstract iii
目錄 iv
圖目錄 viii
表目錄 1
公式目錄 1
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
1.4 研究方法 3
1.5 論文架構 4
第二章文獻探討 6
2.1 網路拍賣詐欺成因與特性 6
2.2 拍賣詐欺分類 7
2.3 社會網路分析 10
2.3.1 社會網路分析分群的指標 10
2.4 拍賣詐欺偵測方法 12
2.5 詐欺共犯群體的特性 14
2.6 網絡探勘(Web Mining) 15
2.6.1 PageRank 16
2.7模糊理論 18
2.7.1模糊推論系統 19
2.7.2模糊化介面（fuzzification interface） 19
2.7.3知識庫（Knowledge Base） 20
2.7.4決策邏輯單元（decision making logic） 22
2.7.5解模糊化介面(defuzzification interface) 24
2.8調適性類神經模糊推論系統(ANFIS) 24
2.8.1 學習演算法 26
2.9小結 28
第三章研究方法 29
3.1研究假設 29
3.2系統架構 30
3.3 資料蒐集 31
3.3.1 資料來源 31
3.3.2 網絡結構搜尋代理人 33
3.4 資料前處理 38
3.5 PageRank演算法 40
3.6 Auction Fraud Rank演算法 42
3.7 調適性類神經模糊推論系統架構(Adaptive-Network-based Fuzzy Inference System, ANFIS) 44
3.7.1定義輸入、輸出變數 44
3.7.2歸屬函數的決定 45
3.7.3 模糊規則的決定 45
3.7.4 學習演算法 47
第四章實驗結果與討論 48
4.1 建置ANFIS模型與驗證ANFIS模型 48
4.2 系統實作與案例說明 62
4.3 相關研究比較 66
4.4 系統效率評估 69
第五章結論與未來研究方向 72
5.1 結論 72
5.2 未來研究方向 73
參考文獻 75
中文部分 75
英文部分 75
網頁部分 82

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網頁部分
[50] 資策會MIC．2010年5月31日取自http://mic.iii.org.tw/aisp/
[51] 雅虎拍賣．2010年5月31日取自http://tw.bid.yahoo.com/
[52] 露天拍賣．2010年5月31日取自http://www.ruten.com.tw/
[53] The internet crime complaint center (IC3)．2010年5月31日取自http://www.ic3.gov/default.aspx

指導教授

林熙禎(Shi-Jen Lin)

審核日期

2010-7-2

推文