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研究生(外文):Ping-Hsien Lin
論文名稱(外文):A Fraud Detection System for Real-time Messaging Communication on Android Facebook Messenger
指導教授(外文):Nai-Wei Lo
口試委員(外文):Nai-Wei Lo
外文關鍵詞:Fraud DetectionLatent Semantic AnalysisCosine Similarity
  • 被引用被引用:1
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  • 下載下載:72
  • 收藏至我的研究室書目清單書目收藏:1
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
[1]Y. Kou, C. T. Lu, S. Sirwongwattana and Y. P. Huang, “Survey of fraud detection techniques,” 2004 IEEE international conference on Networking, sensing and control, vol. 2, pp. 749-754, 2004.
[2]W. Lee and K. W. Mok, “Adaptive intrusion detection: a data mining approach,” Artificial Intelligence Review, vol. 14, no. 6, pp. 533-567, 2000.
[3]M. H. Cahill, D. Lambert, J. C. Pinheiro and D. X. Sun, “Detecting fraud in the real world,” Handbook of massive data sets, pp. 911-929, 2002.
[4]J. B. S. Freeman, A. Bivens and B. Szymanski, “Host-based intrusion detection using user signatures,” Graduate Research Conference, 2002.
[5]S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas and R. A. Harsh-man, “Indexing by Latent Semantic Analysis,” American Society for Information Science, vol. 41, no. 6, pp 391-407, 1990b.
[6]C. D. Manning, P. Raghavan and H. Schütze, “Introduction to information retrieval,” Cambridge: Cambridge university press, 2008.
[7]G. Cosma and M. Joy, “An Approach to Source-Code Plagiarism Detection and Investigation Using Latent Semantic Analysis,” Institute of Electrical and Electronics Engineers Transactions on Computers, vol. 61, no. 3, pp. 379-394, 2012.
[8]N. Evangelopoulos, X. Zhang, and V. R. Prybutok, “Latent Semantic Analysis: Five Methodological Recommendations,” European Journal of Information Sys-tems, vol. 21, no. 1, pp. 70-86, 2010.
[9]T. K. Landauer, D. S. McNamara, S. Dennis and W. Kintsch, Handbook of Latent Semantic Analysis, Psychology Press, 2013.
[10]F.-F. Kuo, M.-K. Shan, and S.-Y. Lee, “Background Music Recommendation for Video Based on Multimodal Latent Semantic Analysis,” 2013 IEEE International Conference on Multimedia and Expo (ICME), pp. 1-6, 2013.
[11]R. Klein, A. Kyrilov and M. Tokman, “Automated Assessment of Short Free-Text Responses in Computer Science using Latent Semantic Analysis,” in Proceedings of the 16th annual joint conference on innovation and technology in computer science education, ACM, pp. 158-162, 2011.
[12]M. C. Lintean, C. Moldovan, V. Rus and D. S. McNamara, “The Role of Local and Global Weighting in Assessing the Semantic Similarity of Texts Using Latent Semantic Analysis,” FLAIRS Conference, pp. 235-240, 2010.
[13]M. G. Ozsoy, F. N. Alpaslan and I. Cicekli, “Text summarization using Latent Semantic Analysis,” Journal of Information Science, vol. 37, no. 4, pp. 405-417, 2011.
[14]C.-J. Luh, S.-A. Yang and D. T.-L. Huang, “Estimating Search Engine Ranking Function with Latent Semantic Analysis and a Genetic Algorithm,” in Proceed-ings of the 2012 3rd International Conference on E-Business and E-Government-Volume 04, IEEE Computer Society, pp. 439-442, 2012.
[15]P. Y. Hui and H. Y. Meng, “Latent Semantic Analysis for Multimodal User Input With Speech and Gestures,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 2, pp. 417-429, 2014.
[16]T. Hofmann, “Probabilistic latent semantic analysis,” in Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, ACM, pp. 289-296, 1999.
[17]J. Zhang and S. Gong, “Action Categorization by Structural Probabilistic Latent Semantic Analysis,” Computer Vision and Image Understanding, vol. 114, no. 8, pp. 857-864, 2010.
[18]C. Shen, T. Li and C. H. Ding, “Integrating Clustering and Multi-Document Summarization by Bi-Mixture Probabilistic Latent Semantic Analysis (PLSA) with Sentence Bases,” Association for the Advancement of Artificial Intelligence, pp. 914-920, 2011.
[19]E. C. Su, J.-M. Chang, C.-W. Cheng, T.-Y. Sung, and W.-L. Hsu, “Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic la-tent semantic indexing,” BMC bioinformatics (13:S17-S13), pp. 1-10, 2012.
[20]Y. Wen, C. Zou and J. Liu, “Probabilistic latent semantic analysis for sketch-based 3D model retrieval," 2014 4th IEEE International Conference on Information Science and Technology (ICIST), pp. 594-597, 2014.
[21]D. M. Blei, A. Y. Ng and M. I. Jordan, “Latent Dirichlet Allocation,” the Journal of machine Learning research, vol. 3, pp. 993-1022, 2003.
[22]A. Bhardwaj, M. Reddy, S. Setlur, V. Govindaraju and S. Ramachandrula, “Latent Dirichlet Allocation Based Writer Identification in Offline Handwriting,” in Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, ACM, pp. 357-362, 2010.
[23]J. C. Niebles, H. Wang, and L. Fei-Fei, “Unsupervised learning of human action categories using spatial-temporal words,” International journal of computer vision, vol. 79, no. 3, pp. 299-318, 2008.
[24]J. Caol, J. Li, Y. Zhang and S. Tang, “LDA-Based Retrieval Framework for Semantic News Video Retrieval,” 2007 IEEE International Conference on Semantic Computing (ICSC), pp. 155-160, 2007.
[25]M. Juneja, A. Vedaldi, C. Jawahar and A. Zisserman, “Blocks that Shout: Distinctive Parts for Scene Classification,” 2013 IEEE Conference on Computer Vi-sion and Pattern Recognition (CVPR), pp. 924-930, 2013.
[26]T. Pang-Ning, M. Steinbach and V. Kumar, “Introduction to data mining,” Library of Congress, 2006.
[27]A. Singhal, “Modern information retrieval: A brief overview,” IEEE Data Eng. Bull, vol. 24, no. 4, pp. 35-43, 2001.
[28]V. Thada and D. V. Jaglan, “Comparison of Jaccard, Dice, Cosine Similarity Coefficient To Find Best Fitness Value for Web Retrieved Documents Using Genet-ic Algorithm,” International Journal of Innovations in Engineering and Technology, 2013.
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2. 尤嫣嫣(1999)。肥胖問題的探討。學校衛生,35,85-99。
3. 林順萍、黃國禎、陳俊忠、郭博昭(2006)。太極拳運動對心臟血管機能之影響。中華技術學院學報,34,389-397。
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6. 郭正典、陳高揚(1997)。心率變異度及心肺功能失常。臨床醫學,39(5),271-274。
7. 陳天香(1994)。階梯有氧運動對大一男女生生理功能的影響。臺大體育,24,5-24。
8. 陳天香(1994)。階梯有氧運動對大學一年級及三、四年級學生體適能影響的差異。臺大體育,25,7-18。
9. 陳全壽(1995)。二十一世紀的身體運動及運動科學研究。國民體育季刊,24(4),4-12。
10. 陳高揚、郭正典、駱惠銘(2000)。心率變異度:原理與應用。中華民國急救加護醫學會雜誌,11(2),47-58。
11. 陳高揚、郭正典(2001)。佛教師子王臥對冠狀動脈疾病患者及孕婦自律神經活性的效應。佛學與科學,2(1),7-20。
12. 黃永任(1998)。運動、體適能與疾病預防。國民體育季刊,27(2),5-13。
13. 黃苹苹、王顯智(2005)。心率變異度在運動上之應用。大專體育,77,63-69。
14. 黃新作(1993)。運動中心臟自律神經功能之變化.以心電圖R-R波間隔變動強度光譜解析法來探討。國立體育學院論叢,4(1),123-138。
15. 張美莉(1995)。10與14週階梯有氧舞蹈及高衝擊有氧舞蹈學習課程對一般大學女生健康體適能影響的探討。大專體育,23,120-131。