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題名 以深度學習探勘社群網路異常使用者的協作行為
Discovering Coordination Behaviors of Malicious Accounts over Social Media Using Deep Learning作者 陳郁雯
Chen, Yu-Wen貢獻者 沈錳坤
Shan, Man-Kwan
陳郁雯
Chen, Yu-Wen關鍵詞 異常帳號
協作行為
深度學習
Malicious Accounts
Coordination
Deep Learning日期 2020 上傳時間 2-九月-2020 13:15:32 (UTC+8) 摘要 近年來社群媒體的興起，訊息經過社群網路快速傳播，使用者各種意見形成公眾輿論。有心人士企圖利用大量的假帳號，操作輿論影響多數人的想法，來達到特定的目的。輿論帶風向者往往透過寫手發文後，由真人或機器人程式，操作大量假帳號，在發文後的短時間內大量的留言，以達到帶風向、製造輿論的目的。本論文根據使用者在社群媒體上留言的共謀行為，研究由已知的異常帳號來探索出未知的同夥異常帳號。我們運用深度學習技術以計算共謀行為的相似度。本論文以國內最大的BBS站PTT為例，實驗PTT 2018年8月至2020年2月八卦版及政黑板的資料。實驗結果顯示本論文的方法可有效地由異常帳號探索出具有協作行為的未知異常帳號。
As social media service is more and more popular, information is shared and spread quickly over the social network. Some try to manipulate the public opinion by means of malicious accounts. It has been reported that one way of public opinion manipulation can be achieved by delivering the stories, and operating large amounts of malicious accounts to promote the stories few minutes after the delivery of story in a short period of time.According to the observation of collusive behaviors of comment operations between malicious accounts over social media, this thesis investigates the exploration by examples approach to explore unknown accomplices by the known malicious accounts. Deep learning technique is leveraged to discover the similarity of collusive behaviors. The experiments were performed based on data collected from PTT Gossiping and HatePolitics board from August 2018 to February 2020. The experimental results show that the proposed mechanism can effectively discover collusive behaviors of malicious accounts.參考文獻 [1] Kayode Sakariyah Adewole, Nor Badrul Anuar, Amirrudin Kamsin, Kasturi Dewi Varathan, Syed Abdul Razak. Malicious Accounts: Dark of the Social Networks. Journal of Network and Computer Applications, Vol. 79, 2017,.[2] Muhammad Al-Qurishi, Mabrook Al-Rakhami, Atif Alamri, Majed Alrubaian, Sk Md Mizanur Rahman, and M. Shamim Hossain. Sybil Defense Techniques in Online Social Networks- A Survey. IEEE Access, Vol. 5, 2017.[3] Qiang Cao, Michael Sirivianos, Xiaowei Yang, and Tiago Pregueiro. Aiding the Detection of Fake Accounts in Large Scale Social Online Services. USENIX/ACM Symposium on Networked Systems Design and Implementation , 2012.[4] Manuel Egele, Gianluca Stringhini, Christopher Kruegel, and Giovanni Vigna. Towards Detecting Compromised Accounts on Social Networks. IEEE Transactions on Dependable and Secure Computing, Vol. 14, Issue 4, 2017.[5] Ahmed Elazab, Mahmood A. Mahmood, Hesham A. HefnyHesham, and A. Hefny. Fake Accounts Detection in Twitter Based on Minimum Weighted Feature set. International Scholarly and Scientific Research and Innovation, Vol. 10, No. 1, 2016.[6] Rodrigo Augusto Igawa, Sylvio Barbon Jr, Kátia Cristina Silva Paulo, Guilherme Sakaji Kido, Rodrigo Capobianco Guido, Mario Lemes Proença Júnior, and Ivan Nunes da Silva. Account Classification in Online Social Networks with LBCA and Wavelets. Information Sciences, Vol. 332, 2016.[7] Sangho Lee, and Jong Kim. Early Filtering of Ephemeral Malicious Accounts on Twitter. Computer Communications, Vol. 54, 2014.[8] Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman. Finding Similar Items. In Mining of Massive Datasets, Cambridge University Press, 2020.[9] Tomas Mikolov, Kai Chen, Greg S. Corrado, and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. International Conference on Learning Representations, 2013.[10] David L. Olson, and Dursun Delen. Performance Evaluation for Predictive Modeling. In: Advanced Data Mining Techniques, Springer, 2008.[11] Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter, Vol. 109, No. 1, 2017.[12] Monika Singh, Divya Bansal, and Sanjeev Sofat. Detecting Malicious Users in Twitter using Classifiers. 7th International Conference on Security of Information and Networks, , 2014,.[13] Bimal Viswanath, Muhammad Ahmad Bashir, Mark Edward, Saikat Guh, Krishna Phani Gummadi, Balachander Krishnamurthy, and Alan Mislove. Towards Detecting Anomalous User Behavior in Online Social Networks. USENIX Conference on Security Symposium, August, 23rd, 2014.[14] Soroush Vosoughi, Deb Roy, and Sinan Aral. The Spread of True and False News Online. Science, Vol. 395, Issue 6380, 2018.[15] Gang Wang, Manish Mohanlal, Christo Wilson, Xiao Wang, Miriam Metzger, Haitao Zheng, and Ben Y. Zhao. Social Turing Tests: Crowdsourcing Sybil Detection. The Network and Distributed System Security Symposium, The Internet Society, 2013.[16] Ming-Hung Wang, Nhut-Lam Nguyen, Shih-chan Dai, Po-Wen Chi, and Chyi-Ren Dow. Understanding Potential Cyber-Armies in Elections: A Study of Taiwan. Sustainability, Vol. 12, No. 6, 2020.[17] Ming-Hung Wang, Nhut-Lam Nguyen, and Chyi-Ren Dow. Detecting Potential Cyber Armies of Election Campaigns Based on Behavioral Analysis. In Complex Networks and Their Applications VII, 2018.[18] Yahan Wang, Chunhua Wu, Kangfeng Zheng, and Xiujuan Wang. Social Bot Detection Using Tweets Similarity. Security and Privacy in Communication Networks, 2018.[19] Haifeng Yu, Phillip B. Gibbons, Michael Kaminsky, and Feng Xiao. SybilLimit: A Near-Optimal Social Network Defense Against Sybil Attacks. IEEE/ACM Transactions on Networking, Vol. 18, No. 3, 2010.[20] Haifeng Yu, Michael Kaminsky, Phillip B. Gibbons, and Abraham D. Flaxman. SybilGuard: Defending against Sybil Attacks via Social Networks. IEEE/ACM Transactions on Networking, Vol. 16, 2008.[21] Xinyi Zhou, Reza Zafarani, Kai Shu, and Huan Liu. Fake News: Fundamental Theories, Detection Strategies and Challenges. 12th ACM International Conference on Web Search and Data Mining, 2019.[22] 孔德廉，誰帶風向：被金錢操弄的公共輿論戰爭，報導者，2018/09/26。[23] 孔德廉，網紅、假帳號、素人暗樁──值得信賴的口碑行銷？，報導者，2018/09/26。[24] 林倖妃，一個帳號幾多錢，網軍價格全揭露，天下雜誌671期，2019/04/24。[25] 林佳賢，跟著資料記者追網軍，「假外國人」如何在PTT鼓吹韓流，天下雜誌671期，2019/04/24。 描述 碩士
國立政治大學
資訊科學系碩士在職專班
106971016資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106971016 資料類型 thesis dc.contributor.advisor 沈錳坤 zh_TW dc.contributor.advisor Shan, Man-Kwan en_US dc.contributor.author (作者) 陳郁雯 zh_TW dc.contributor.author (作者) Chen, Yu-Wen en_US dc.creator (作者) 陳郁雯 zh_TW dc.creator (作者) Chen, Yu-Wen en_US dc.date (日期) 2020 en_US dc.date.accessioned 2-九月-2020 13:15:32 (UTC+8) - dc.date.available 2-九月-2020 13:15:32 (UTC+8) - dc.date.issued (上傳時間) 2-九月-2020 13:15:32 (UTC+8) - dc.identifier (其他 識別碼) G0106971016 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131938 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系碩士在職專班 zh_TW dc.description (描述) 106971016 zh_TW dc.description.abstract (摘要) 近年來社群媒體的興起，訊息經過社群網路快速傳播，使用者各種意見形成公眾輿論。有心人士企圖利用大量的假帳號，操作輿論影響多數人的想法，來達到特定的目的。輿論帶風向者往往透過寫手發文後，由真人或機器人程式，操作大量假帳號，在發文後的短時間內大量的留言，以達到帶風向、製造輿論的目的。本論文根據使用者在社群媒體上留言的共謀行為，研究由已知的異常帳號來探索出未知的同夥異常帳號。我們運用深度學習技術以計算共謀行為的相似度。本論文以國內最大的BBS站PTT為例，實驗PTT 2018年8月至2020年2月八卦版及政黑板的資料。實驗結果顯示本論文的方法可有效地由異常帳號探索出具有協作行為的未知異常帳號。 zh_TW dc.description.abstract (摘要) As social media service is more and more popular, information is shared and spread quickly over the social network. Some try to manipulate the public opinion by means of malicious accounts. It has been reported that one way of public opinion manipulation can be achieved by delivering the stories, and operating large amounts of malicious accounts to promote the stories few minutes after the delivery of story in a short period of time.According to the observation of collusive behaviors of comment operations between malicious accounts over social media, this thesis investigates the exploration by examples approach to explore unknown accomplices by the known malicious accounts. Deep learning technique is leveraged to discover the similarity of collusive behaviors. The experiments were performed based on data collected from PTT Gossiping and HatePolitics board from August 2018 to February 2020. The experimental results show that the proposed mechanism can effectively discover collusive behaviors of malicious accounts. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究背景 11.2 研究動機與目的 21.3 論文貢獻 3第二章 相關研究 4第三章 研究方法 83.1 研究架構 83.2 資料蒐集 93.3 資料前處理 113.4 建立模型 133.5 應用：相似度計算 15第四章 實驗結果分析 174.1 資料觀察 174.2 不同留言情形比較 284.3 不同Window Size 與Dimension的影響 324.4 與Jaccard Coefficient比較 34第五章 結論與未來研究 375.1 結論 375.2 未來研究 37參考文獻 38 zh_TW dc.format.extent 4097978 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106971016 en_US dc.subject (關鍵詞) 異常帳號 zh_TW dc.subject (關鍵詞) 協作行為 zh_TW dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) Malicious Accounts en_US dc.subject (關鍵詞) Coordination en_US dc.subject (關鍵詞) Deep Learning en_US dc.title (題名) 以深度學習探勘社群網路異常使用者的協作行為 zh_TW dc.title (題名) Discovering Coordination Behaviors of Malicious Accounts over Social Media Using Deep Learning en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Kayode Sakariyah Adewole, Nor Badrul Anuar, Amirrudin Kamsin, Kasturi Dewi Varathan, Syed Abdul Razak. Malicious Accounts: Dark of the Social Networks. Journal of Network and Computer Applications, Vol. 79, 2017,.[2] Muhammad Al-Qurishi, Mabrook Al-Rakhami, Atif Alamri, Majed Alrubaian, Sk Md Mizanur Rahman, and M. Shamim Hossain. Sybil Defense Techniques in Online Social Networks- A Survey. IEEE Access, Vol. 5, 2017.[3] Qiang Cao, Michael Sirivianos, Xiaowei Yang, and Tiago Pregueiro. Aiding the Detection of Fake Accounts in Large Scale Social Online Services. USENIX/ACM Symposium on Networked Systems Design and Implementation , 2012.[4] Manuel Egele, Gianluca Stringhini, Christopher Kruegel, and Giovanni Vigna. Towards Detecting Compromised Accounts on Social Networks. IEEE Transactions on Dependable and Secure Computing, Vol. 14, Issue 4, 2017.[5] Ahmed Elazab, Mahmood A. Mahmood, Hesham A. HefnyHesham, and A. Hefny. Fake Accounts Detection in Twitter Based on Minimum Weighted Feature set. International Scholarly and Scientific Research and Innovation, Vol. 10, No. 1, 2016.[6] Rodrigo Augusto Igawa, Sylvio Barbon Jr, Kátia Cristina Silva Paulo, Guilherme Sakaji Kido, Rodrigo Capobianco Guido, Mario Lemes Proença Júnior, and Ivan Nunes da Silva. Account Classification in Online Social Networks with LBCA and Wavelets. Information Sciences, Vol. 332, 2016.[7] Sangho Lee, and Jong Kim. Early Filtering of Ephemeral Malicious Accounts on Twitter. Computer Communications, Vol. 54, 2014.[8] Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman. Finding Similar Items. In Mining of Massive Datasets, Cambridge University Press, 2020.[9] Tomas Mikolov, Kai Chen, Greg S. Corrado, and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. International Conference on Learning Representations, 2013.[10] David L. Olson, and Dursun Delen. Performance Evaluation for Predictive Modeling. In: Advanced Data Mining Techniques, Springer, 2008.[11] Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter, Vol. 109, No. 1, 2017.[12] Monika Singh, Divya Bansal, and Sanjeev Sofat. Detecting Malicious Users in Twitter using Classifiers. 7th International Conference on Security of Information and Networks, , 2014,.[13] Bimal Viswanath, Muhammad Ahmad Bashir, Mark Edward, Saikat Guh, Krishna Phani Gummadi, Balachander Krishnamurthy, and Alan Mislove. Towards Detecting Anomalous User Behavior in Online Social Networks. USENIX Conference on Security Symposium, August, 23rd, 2014.[14] Soroush Vosoughi, Deb Roy, and Sinan Aral. The Spread of True and False News Online. Science, Vol. 395, Issue 6380, 2018.[15] Gang Wang, Manish Mohanlal, Christo Wilson, Xiao Wang, Miriam Metzger, Haitao Zheng, and Ben Y. Zhao. Social Turing Tests: Crowdsourcing Sybil Detection. The Network and Distributed System Security Symposium, The Internet Society, 2013.[16] Ming-Hung Wang, Nhut-Lam Nguyen, Shih-chan Dai, Po-Wen Chi, and Chyi-Ren Dow. Understanding Potential Cyber-Armies in Elections: A Study of Taiwan. Sustainability, Vol. 12, No. 6, 2020.[17] Ming-Hung Wang, Nhut-Lam Nguyen, and Chyi-Ren Dow. Detecting Potential Cyber Armies of Election Campaigns Based on Behavioral Analysis. In Complex Networks and Their Applications VII, 2018.[18] Yahan Wang, Chunhua Wu, Kangfeng Zheng, and Xiujuan Wang. Social Bot Detection Using Tweets Similarity. Security and Privacy in Communication Networks, 2018.[19] Haifeng Yu, Phillip B. Gibbons, Michael Kaminsky, and Feng Xiao. SybilLimit: A Near-Optimal Social Network Defense Against Sybil Attacks. IEEE/ACM Transactions on Networking, Vol. 18, No. 3, 2010.[20] Haifeng Yu, Michael Kaminsky, Phillip B. Gibbons, and Abraham D. Flaxman. SybilGuard: Defending against Sybil Attacks via Social Networks. IEEE/ACM Transactions on Networking, Vol. 16, 2008.[21] Xinyi Zhou, Reza Zafarani, Kai Shu, and Huan Liu. Fake News: Fundamental Theories, Detection Strategies and Challenges. 12th ACM International Conference on Web Search and Data Mining, 2019.[22] 孔德廉，誰帶風向：被金錢操弄的公共輿論戰爭，報導者，2018/09/26。[23] 孔德廉，網紅、假帳號、素人暗樁──值得信賴的口碑行銷？，報導者，2018/09/26。[24] 林倖妃，一個帳號幾多錢，網軍價格全揭露，天下雜誌671期，2019/04/24。[25] 林佳賢，跟著資料記者追網軍，「假外國人」如何在PTT鼓吹韓流，天下雜誌671期，2019/04/24。 zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202001668 en_US
