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Author: 林秉賢
Ping-Hsien Lin
Thesis Title: 一個偵測行動裝置即時通訊訊息的反詐騙系統-以臉書即時通為例
A Fraud Detection System for Real-time Messaging Communication on Android Facebook Messenger
Advisor: 羅乃維
Nai-Wei Lo
Committee: 吳宗成
Tzong-Chen Wu
葉國暉
Kuo-Hui Yeh
Degree: 碩士
Master
Department: 管理學院 - 資訊管理系
Department of Information Management
Thesis Publication Year: 2015
Graduation Academic Year: 103
Language: 英文
Pages: 42
Keywords (in Chinese): 詐騙偵測潛在語意模型餘弦相似度
Keywords (in other languages): Fraud Detection, Latent Semantic Analysis, Cosine Similarity
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  • 隨著智慧型手機的普及化,各種行動應用裝置通訊應用程式(如:Facebook、Line、WeChat)的興起,不僅縮短了人與人溝通的距離,也節省了許多通訊的成本。但是在享受資訊科技所帶來便利的同時,許多風險也隨之產生,除了一些高風險的應用程式權限,導致我們的個人隱私資訊洩露之外,也被詐騙集團拿來當作詐騙的工具。近年來,許多詐騙事件都是詐騙集團透過行動應用裝置通訊應用程式來犯罪,利用聊天的方式掌握人性的弱點,進而騙取錢財。
    在本篇論文中,我們設計出一個偵測行動裝置即時通訊訊息的反詐騙系統-以臉書即時通為例來解決上述的詐騙問題。本系統使用自然語言處理、矩陣處理、潛在語意分析與餘弦相似度來處理所輸入的資料,並且蒐集許多詐騙相關的新聞與案例,來驗證本系統偵測詐騙事件可行性,最後透過本系統搭配的行動裝置應用程式,達成警示使用者該聊天紀錄是否為詐騙事件的效果。


    Recently, the popularity rate of the smartphone usage has rapidly risen. There is a variety of mobile applications which are developed, such as “Facebook”, “Line”, “WeChat”, etc. The applications not only make people communicate with each other more easily, but also help humans reduce extra fee of calling or sending short messages. However, when we enjoy the convenience of the smartphone, many potential risks will appear at the same time. For example, some of high risk permissions would let your personal privacy information be exposed. In Taiwan, fraudsters also use the applications as a fraud tool to complete their purpose of crime.
    In this paper, we develop a fraud detection system of communications to solve the fraud problems. We use some technologies to process input data and verify feasibility of the fraud detection system, such as natural language processing, matrix processing, latent semantic analysis and cosine similarity. Then, we collect some news and cases about fraud event as training data for our fraud detection system and intercept the real-time message chat logs from “Facebook Messenger” as testing data. Finally, we develop a mobile application to warn the user whether the real-time message chat logs are fraud event or not.

    中文摘要 I Abstract II 誌謝 III Contents IV List of Figures V List of Tables VI Chapter 1 Introduction 1 Chapter 2 Preliminaries 5 2.1 Semantic Models 5 2.1.1 Latent Semantic Analysis 5 2.1.2 Probabilistic Latent Semantic Analysis 6 2.1.3 Latent Dirichlet Allocation 6 2.2 Decision Models 8 2.2.1 Cosine similarity 8 2.2.2 Jaccard Similarity 9 2.2.3 Dice Similarity 9 Chapter 3 The Proposed Fraud Detection System 10 3.1 System Architecture 10 3.2 Data Flow of the Fraud Detection System 10 3.3 Data Collection 11 3.4 Natural Language Processing 12 3.4.1 CKIP Word Segmentation 12 3.4.2 Stop Word 13 3.4.3 Special Symbol 13 3.5 Matrix Processing 13 3.5.1 Vector Space Model (VSM) 13 3.5.2 Term Frequency-Inverse Document Frequency Matrix 16 3.6 Latent Semantic Analysis 20 3.7 Classification Rules 28 Chapter 4 System Implementation, Testing Scenarios and Discussion 31 4.1 System Implementation 31 4.2 Testing Scenarios 33 4.3 Discussion 37 Chapter 5 Conclusion 38 References 39

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