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題名 利用時間序列的可疑時間點探勘社群媒體的盜用帳號
Exploring Compromised Accounts in Social Media by Time Series Change Point Detection作者 吳宗樺
Wu, Tsung-Hua貢獻者 沈錳坤
Shan, Man-kwan
吳宗樺
Wu, Tsung-Hua關鍵詞 變化點偵測
盜用帳號偵測日期 2022 上傳時間 5-Oct-2022 09:15:07 (UTC+8) 摘要 有鑒於社群媒體傳播的重要性與影響與日俱增，逐漸形塑出我們對於整個世界的認知，在社群媒體上也盛行利用大量網軍及假帳號的協同性造假行為來帶動輿論風向，而其中針對社群媒體帳號遭盜用或是帳號遭盜用後被拍賣牟利這類情形，就屬於盜用帳號偵測的問題。本論文旨在研究社群媒體上盜用帳號的探勘方法。本論文所提出的方法主要運用盜用可疑時間點偵測。第一階段先根據帳號的活動量、活動時間等時間行為特徵，結合統計學領域的變化點偵測，進行可疑時間點偵測，以找到帳號行為改變的可疑時間點。第二階段，針對可疑時間點數量，以及每個可疑時間點前後的時間行為特徵、空間行為特徵、文字內容、立場光譜等9項特徵是否有明顯差異變化進行檢驗。最後，則透過模擬生成盜用帳號方法，去驗證預測模型之效果。最後，我們先以Twitter為例，證明在真實資料集狀況下，我們的研究方法也能有較好的表現。接著以PTT人工生成資料方式，驗證運用可疑時間點偵測及所提出的行為特徵之效果。
In view of the increasing importance and influence of social media communication, which has gradually shaped our cognition of the whole world, it is also become popular in social media to use a large number of cyber warriors and fake accounts to manipulate public opinion. Social media accounts that are hijacked or auctioned for profit is considered as the compromised accounts.This study aims to research the detection method of social media compromised account. In the first step, based on the activity volume and activity time of the account, we propose the integration of change point detection algorithm to find the suspicious time point when the behavior of account changes. In the second step, we propose the interest feature, the polarity feature and the change point feature and examine whether there is major change in the account`s behavioral feature before and after the suspicious time point. Finally, the prediction model is validated by simulating the generation of compromised accounts.We verify the proposed change point approach by using Twitter Dataset. Then, we verify the performance of the proposed features by using synthesized PTT dataset. The experiments show that the proposed approach performs better than existing research.參考文獻 [1] S. Ahmad, A. Lavin, S. Purdy, and Z. Agha, Unsupervised Real-Time Anomaly Detection for Streaming Data. Neurocomputing, Vol. 262, 2017.[2] A. Alharbi, H. Dong, X. Yi, Z. Tari, and I. Khalil, Social Media Identity Deception Detection: A Survey. ACM Computing Surveys, Vol. 54, No. 3, 2022.[3] S. Aminikhanghahi and D. J. Cook, A Survey of Methods for Time Series Change Point Detection, Knowledge and Information Systems, Vol. 51, 2017.[4] J. An, and S. Cho, Variational Autoencoder Based Anomaly Detection Using Reconstruction Probability. Special Lecture on IE, Vol. 2, No. 1, 2015.[5] F. Angiulli, and C. Pizzuti, Fast Outlier Detection in High Dimensional Spaces. The 6th European Conference on Principles of Data Mining and Knowledge Discovery, 2002.[6] M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, LOF: Identifying Density-Based Local Outliers. The 2000 ACM Special Interest Group on Management of Data (SIGMOD) International Conference on Management of Data, 2000.[7] J. Cabrieto, F. Tuerlinckx, P. Kuppens, F. H. Wilhelm, M. Liedlgruber, and E. Ceulemans, Capturing Correlation Changes by Applying Kernel Change Point Detection on the Running Correlations. Information Science, Vol. 447, 2018.[8] Q. Cao, X. Yang, J. Yu, and C. Palow, Uncovering Large Groups of Active Malicious Account in Online Social Networks. The 2014 ACM Special Interest Group on Security, Audit and Control (SIGSAC) Conference on Computer and Communications Security, 2014.[9] V. Chandola, A. Banerjee, and V. Kumar, Anomaly Detection: A Survey. ACM Computing Surveys, Vol. 41, No. 3, 2009.[10] V. Chandola, and R. R. Vatsavai, Scalable Time Series Change Detection for Biomass Monitoring Using Gaussian Process. Conference on Intelligent Data Understanding, 2010.[11] I. Clarke, and J. Grieve, Stylistic Variation on the Donald Trump Twitter Account: A Linguistic Analysis of Tweets Posted between 2009 and 2018. PLoS ONE, Vol. 14, 2019.[12] I. Cleland, M. Han, C. Nugent, H. Lee, S. McClean, S. Zhang, and S. Lee, Evaluation of Prompted Annotation of Activity Data Recorded from a Smart Phone. Sensors (Basel, Switzerland), Vol. 14, 2014.[13] D. J. Cook, N. C. Krishnan, Activity Learning: Discovering Recognizing and Predicting Human Behavior from Sensor Data. Wiley,2015[14] F. Desobry, M. Davy and C. Doncarli, An Online Kernel Change Detection Algorithm. IEEE Transactions on Signal Processing, Vol. 53, No. 8, 2005[15] M. Egele, G. Stringhini, C. Kruegel, and G. Vigna, COMPA: Detecting Compromised Accounts on Social Networks, Network and Distributed System Security (NDSS) Symposium, 2013.[16] E. Ferrara, O. Varol, C. Davis, F. Menczer, and A. Flammini, The Rise of Social Bots. Communications of the ACM, Vol. 59, No. 7, 2016.[17] K. D. Feuz, D. J. Cook, C. Rosasco, K. Robertson and M. Schmitter-Edgecombe, Automated Detection of Activity Transitions for Prompting. IEEE Transactions on Human-Machine Systems, Vol. 45, No. 5, 2015.[18] A. Geiger, D. Liu, S. Alnegheimish, A. Cuesta-Infante and K. Veeramachaneni, TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks. 2020 IEEE International Conference on Big Data, 2020.[19] S. Gokalp, M. Temkit, H. Davulcu, and I. H. Toroslu, Partitioning and Scaling Signed Bipartite Graphs for Polarized Political Blogosphere. IEEE 2013 International Conference on Social Computing, 2013.[20] M. Goldstein, and S. Uchida, A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. PLOS One, Vol. 11, No. 4, 2016.[21] Y. Guédon, Exploring the Latent Segmentation Space for the Assessment of Multiple Change-Point Models. Computational Statistics, Vol. 28, Iss. 6, 2013.[22] R. A. A. Habeeb, F. Nasaruddin, A. Gani, I. A. T. Hashem, E. Ahmed, and M. Imran, Real-time Big Data Processing for Anomaly Detection: A Survey. International Journal of Information Management, Vol. 45, 2019.[23] M. Han, L. T. Vinh, Y. K. Lee, and S. Lee, Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone. Sensors (Basel, Switzerland), Vol. 12, 2012.[24] Z. He, X. Xu, and S. Deng, Discovering Cluster-Based Local Outliers. Pattern Recognition Letters, Vol. 24, No. 9-10, 2003.[25] S. Hido, T. Idé, H. Kashima, H. Kubo, and H. Matsuzawa, Unsupervised Change Analysis Using Supervised Learning. The 12th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, 2008.[26] V. Hodge, and J. Austin, A Survey of Outlier Detection Methodologies. Artificial Intelligence Review, Vol. 22, No. 2, 2004.[27] K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding. The 24th ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), 2018.[28] H. Karimi, C. VanDam, L. Ye, and J. Tang, End-to-End Compromised Account Detection, ACM/IEEE International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018.[29] R. Kaur, S. Singh, and H. Kumar, AuthCom: Authorship Verification and Compromised Account Detection in Online Social Networks Using AHP-TOPSIS Embedded Profiling Based Technique. Expert Systems with Applications, Vol. 113, 2018.[30] R. Kaur, S. Singh, and H. Kumar, TB-CoAuth: Text Based Continuous Authentication for Detecting Compromised Accounts in Social Networks, Applied Soft Computing, Vol. 97, Part. A, 2020.[31] Y. Kawahara and M. Sugiyama, Sequential Change-Point Detection Based on Direct Density-Ratio Estimation. Statistical Analysis and Data Mining, Vol 5, Iss. 2, 2012.[32] H. F. Lau and S. Yamamoto, Bayesian Online Change Point Detection to Improve Transparency in Human-Machine Interaction Systems. 49th IEEE Conference on Decision and Control (CDC), 2010.[33] P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff, LSTM-Based Encoder-Decoder for Multi-Sensor Anomaly Detection. Anomaly Detection Workshop at 33rd ICML, 2016.[34] E. H. M. Pena, M. V. O. de Assis and M. L. Proença, Anomaly Detection Using Forecasting Methods ARIMA and HWDS. 32nd International Conference of the Chilean Computer Science Society, 2013.[35] S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava, Using Mobile Phones to Determine Transportation Modes. ACM Transactions on Sensor Networks, Vol. 6, Iss. 2, 2010.[36] H. Ringberg, A. Soule, J. Rexford, and C. Diot, Sensitivity of PCA for Traffic Anomaly Detection. ACM SIGMETRICS Performance Evaluation Review, Vol. 35, Iss. 1, 2007.[37] P. R. Rosenbaum, An Exact Distribution-Free Test Comparing Two Multivariate Distributions Based on Adjacency. Journal of the Royal Statistical Society Series B, 2005[38] X. Ruan, Z. Wu, H. Wang and S. Jajodia, Profiling Online Social Behaviors for Compromised Account Detection. IEEE Transactions on Information Forensics and Security, Vol. 11, No. 1, 2016.[39] D. Seyler, L. Li, and C. X. Zhai, Semantic Text Analysis for Detection of Compromised Account on Social Networks. The 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2020.[40] M. Singh, D. Bansal, and S. Sofat, Who is Who on Twitter–Spammer, Fake or Compromised Account? A Tool to Reveal True Identity in Real-Time. Cybernetics and Systems, Vol. 49, Iss. 1, 2018.[41] J. M. Torres, P. G. Nieto, L. Alejano, and A. Reyes, Detection of Outliers in Gas Emissions from Urban Areas Using Functional Data Analysis. Journal of Hazardous Materials, Vol. 186, No. 1, 2011.[42] C. Truong, L. Oudre, and N. Vayatis, Selective Review of Offline Change Point Detection Methods, Signal Processing, 2020.[43] C. Wang, H. Zhu, and B. Yang, Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks. IEEE Transactions on Computational Social Systems, 2021.[44] L. Wei and E. Keogh, Semi-Supervised Time Series Classification. The 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006.[45] E. Zangerle and G. Specht, "Sorry, I was Hacked": a Classification of Compromised Twitter Account. the 29th Annual ACM Symposium on Applied Computing, 2014.[46] Y. Zheng, L. Liu, L. Wang, and X. Xie, Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web. The 17th International Conference on World Wide Web, 2008.[47] 黃懷萱，利用行為脈絡探索社群媒體上的異常使用者，國立政治大學資訊科學系碩士論文，2020。 描述 碩士
國立政治大學
資訊科學系
109753118資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109753118 資料類型 thesis dc.contributor.advisor 沈錳坤 zh_TW dc.contributor.advisor Shan, Man-kwan en_US dc.contributor.author (Authors) 吳宗樺 zh_TW dc.contributor.author (Authors) Wu, Tsung-Hua en_US dc.creator (作者) 吳宗樺 zh_TW dc.creator (作者) Wu, Tsung-Hua en_US dc.date (日期) 2022 en_US dc.date.accessioned 5-Oct-2022 09:15:07 (UTC+8) - dc.date.available 5-Oct-2022 09:15:07 (UTC+8) - dc.date.issued (上傳時間) 5-Oct-2022 09:15:07 (UTC+8) - dc.identifier (Other Identifiers) G0109753118 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/142124 - 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 zh_TW dc.description (描述) 109753118 zh_TW dc.description.abstract (摘要) 有鑒於社群媒體傳播的重要性與影響與日俱增，逐漸形塑出我們對於整個世界的認知，在社群媒體上也盛行利用大量網軍及假帳號的協同性造假行為來帶動輿論風向，而其中針對社群媒體帳號遭盜用或是帳號遭盜用後被拍賣牟利這類情形，就屬於盜用帳號偵測的問題。本論文旨在研究社群媒體上盜用帳號的探勘方法。本論文所提出的方法主要運用盜用可疑時間點偵測。第一階段先根據帳號的活動量、活動時間等時間行為特徵，結合統計學領域的變化點偵測，進行可疑時間點偵測，以找到帳號行為改變的可疑時間點。第二階段，針對可疑時間點數量，以及每個可疑時間點前後的時間行為特徵、空間行為特徵、文字內容、立場光譜等9項特徵是否有明顯差異變化進行檢驗。最後，則透過模擬生成盜用帳號方法，去驗證預測模型之效果。最後，我們先以Twitter為例，證明在真實資料集狀況下，我們的研究方法也能有較好的表現。接著以PTT人工生成資料方式，驗證運用可疑時間點偵測及所提出的行為特徵之效果。 zh_TW dc.description.abstract (摘要) In view of the increasing importance and influence of social media communication, which has gradually shaped our cognition of the whole world, it is also become popular in social media to use a large number of cyber warriors and fake accounts to manipulate public opinion. Social media accounts that are hijacked or auctioned for profit is considered as the compromised accounts.This study aims to research the detection method of social media compromised account. In the first step, based on the activity volume and activity time of the account, we propose the integration of change point detection algorithm to find the suspicious time point when the behavior of account changes. In the second step, we propose the interest feature, the polarity feature and the change point feature and examine whether there is major change in the account`s behavioral feature before and after the suspicious time point. Finally, the prediction model is validated by simulating the generation of compromised accounts.We verify the proposed change point approach by using Twitter Dataset. Then, we verify the performance of the proposed features by using synthesized PTT dataset. The experiments show that the proposed approach performs better than existing research. en_US dc.description.tableofcontents 致謝 i摘要 iiAbstract iii目次 iv表次 v圖次 vi第一章 緒論 1第一節 研究背景 1第二節 研究目的 4第二章 相關研究 8第一節 Compromised Account Detection 8第二節 Change Point Detection Methods 10第三章 研究方法 12第一節 研究架構 12第二節 使用者行為特徵提取 14第三節 可疑時間點偵測 25第四節 行為特徵表示法 28第四章 實驗設計 30第一節 Twitter資料 30第二節 PTT資料 33第五章 結論與未來研究 45參考資料 46 zh_TW dc.format.extent 2717294 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109753118 en_US dc.subject (關鍵詞) 變化點偵測 zh_TW dc.subject (關鍵詞) 盜用帳號偵測 zh_TW dc.title (題名) 利用時間序列的可疑時間點探勘社群媒體的盜用帳號 zh_TW dc.title (題名) Exploring Compromised Accounts in Social Media by Time Series Change Point Detection en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] S. Ahmad, A. Lavin, S. Purdy, and Z. Agha, Unsupervised Real-Time Anomaly Detection for Streaming Data. Neurocomputing, Vol. 262, 2017.[2] A. Alharbi, H. Dong, X. Yi, Z. Tari, and I. Khalil, Social Media Identity Deception Detection: A Survey. ACM Computing Surveys, Vol. 54, No. 3, 2022.[3] S. Aminikhanghahi and D. J. Cook, A Survey of Methods for Time Series Change Point Detection, Knowledge and Information Systems, Vol. 51, 2017.[4] J. An, and S. Cho, Variational Autoencoder Based Anomaly Detection Using Reconstruction Probability. Special Lecture on IE, Vol. 2, No. 1, 2015.[5] F. Angiulli, and C. Pizzuti, Fast Outlier Detection in High Dimensional Spaces. The 6th European Conference on Principles of Data Mining and Knowledge Discovery, 2002.[6] M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, LOF: Identifying Density-Based Local Outliers. The 2000 ACM Special Interest Group on Management of Data (SIGMOD) International Conference on Management of Data, 2000.[7] J. Cabrieto, F. Tuerlinckx, P. Kuppens, F. H. Wilhelm, M. Liedlgruber, and E. Ceulemans, Capturing Correlation Changes by Applying Kernel Change Point Detection on the Running Correlations. Information Science, Vol. 447, 2018.[8] Q. Cao, X. Yang, J. Yu, and C. Palow, Uncovering Large Groups of Active Malicious Account in Online Social Networks. The 2014 ACM Special Interest Group on Security, Audit and Control (SIGSAC) Conference on Computer and Communications Security, 2014.[9] V. Chandola, A. Banerjee, and V. Kumar, Anomaly Detection: A Survey. ACM Computing Surveys, Vol. 41, No. 3, 2009.[10] V. Chandola, and R. R. Vatsavai, Scalable Time Series Change Detection for Biomass Monitoring Using Gaussian Process. Conference on Intelligent Data Understanding, 2010.[11] I. Clarke, and J. Grieve, Stylistic Variation on the Donald Trump Twitter Account: A Linguistic Analysis of Tweets Posted between 2009 and 2018. PLoS ONE, Vol. 14, 2019.[12] I. Cleland, M. Han, C. Nugent, H. Lee, S. McClean, S. Zhang, and S. Lee, Evaluation of Prompted Annotation of Activity Data Recorded from a Smart Phone. Sensors (Basel, Switzerland), Vol. 14, 2014.[13] D. J. Cook, N. C. Krishnan, Activity Learning: Discovering Recognizing and Predicting Human Behavior from Sensor Data. Wiley,2015[14] F. Desobry, M. Davy and C. Doncarli, An Online Kernel Change Detection Algorithm. IEEE Transactions on Signal Processing, Vol. 53, No. 8, 2005[15] M. Egele, G. Stringhini, C. Kruegel, and G. Vigna, COMPA: Detecting Compromised Accounts on Social Networks, Network and Distributed System Security (NDSS) Symposium, 2013.[16] E. Ferrara, O. Varol, C. Davis, F. Menczer, and A. Flammini, The Rise of Social Bots. Communications of the ACM, Vol. 59, No. 7, 2016.[17] K. D. Feuz, D. J. Cook, C. Rosasco, K. Robertson and M. Schmitter-Edgecombe, Automated Detection of Activity Transitions for Prompting. IEEE Transactions on Human-Machine Systems, Vol. 45, No. 5, 2015.[18] A. Geiger, D. Liu, S. Alnegheimish, A. Cuesta-Infante and K. Veeramachaneni, TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks. 2020 IEEE International Conference on Big Data, 2020.[19] S. Gokalp, M. Temkit, H. Davulcu, and I. H. Toroslu, Partitioning and Scaling Signed Bipartite Graphs for Polarized Political Blogosphere. IEEE 2013 International Conference on Social Computing, 2013.[20] M. Goldstein, and S. Uchida, A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. PLOS One, Vol. 11, No. 4, 2016.[21] Y. Guédon, Exploring the Latent Segmentation Space for the Assessment of Multiple Change-Point Models. Computational Statistics, Vol. 28, Iss. 6, 2013.[22] R. A. A. Habeeb, F. Nasaruddin, A. Gani, I. A. T. Hashem, E. Ahmed, and M. Imran, Real-time Big Data Processing for Anomaly Detection: A Survey. International Journal of Information Management, Vol. 45, 2019.[23] M. Han, L. T. Vinh, Y. K. Lee, and S. Lee, Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone. Sensors (Basel, Switzerland), Vol. 12, 2012.[24] Z. He, X. Xu, and S. Deng, Discovering Cluster-Based Local Outliers. Pattern Recognition Letters, Vol. 24, No. 9-10, 2003.[25] S. Hido, T. Idé, H. Kashima, H. Kubo, and H. Matsuzawa, Unsupervised Change Analysis Using Supervised Learning. The 12th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, 2008.[26] V. Hodge, and J. Austin, A Survey of Outlier Detection Methodologies. Artificial Intelligence Review, Vol. 22, No. 2, 2004.[27] K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding. The 24th ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), 2018.[28] H. Karimi, C. VanDam, L. Ye, and J. Tang, End-to-End Compromised Account Detection, ACM/IEEE International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018.[29] R. Kaur, S. Singh, and H. Kumar, AuthCom: Authorship Verification and Compromised Account Detection in Online Social Networks Using AHP-TOPSIS Embedded Profiling Based Technique. Expert Systems with Applications, Vol. 113, 2018.[30] R. Kaur, S. Singh, and H. Kumar, TB-CoAuth: Text Based Continuous Authentication for Detecting Compromised Accounts in Social Networks, Applied Soft Computing, Vol. 97, Part. A, 2020.[31] Y. Kawahara and M. Sugiyama, Sequential Change-Point Detection Based on Direct Density-Ratio Estimation. Statistical Analysis and Data Mining, Vol 5, Iss. 2, 2012.[32] H. F. Lau and S. Yamamoto, Bayesian Online Change Point Detection to Improve Transparency in Human-Machine Interaction Systems. 49th IEEE Conference on Decision and Control (CDC), 2010.[33] P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff, LSTM-Based Encoder-Decoder for Multi-Sensor Anomaly Detection. Anomaly Detection Workshop at 33rd ICML, 2016.[34] E. H. M. Pena, M. V. O. de Assis and M. L. Proença, Anomaly Detection Using Forecasting Methods ARIMA and HWDS. 32nd International Conference of the Chilean Computer Science Society, 2013.[35] S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava, Using Mobile Phones to Determine Transportation Modes. ACM Transactions on Sensor Networks, Vol. 6, Iss. 2, 2010.[36] H. Ringberg, A. Soule, J. Rexford, and C. Diot, Sensitivity of PCA for Traffic Anomaly Detection. ACM SIGMETRICS Performance Evaluation Review, Vol. 35, Iss. 1, 2007.[37] P. R. Rosenbaum, An Exact Distribution-Free Test Comparing Two Multivariate Distributions Based on Adjacency. Journal of the Royal Statistical Society Series B, 2005[38] X. Ruan, Z. Wu, H. Wang and S. Jajodia, Profiling Online Social Behaviors for Compromised Account Detection. IEEE Transactions on Information Forensics and Security, Vol. 11, No. 1, 2016.[39] D. Seyler, L. Li, and C. X. Zhai, Semantic Text Analysis for Detection of Compromised Account on Social Networks. The 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2020.[40] M. Singh, D. Bansal, and S. Sofat, Who is Who on Twitter–Spammer, Fake or Compromised Account? A Tool to Reveal True Identity in Real-Time. Cybernetics and Systems, Vol. 49, Iss. 1, 2018.[41] J. M. Torres, P. G. Nieto, L. Alejano, and A. Reyes, Detection of Outliers in Gas Emissions from Urban Areas Using Functional Data Analysis. Journal of Hazardous Materials, Vol. 186, No. 1, 2011.[42] C. Truong, L. Oudre, and N. Vayatis, Selective Review of Offline Change Point Detection Methods, Signal Processing, 2020.[43] C. Wang, H. Zhu, and B. Yang, Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks. IEEE Transactions on Computational Social Systems, 2021.[44] L. Wei and E. Keogh, Semi-Supervised Time Series Classification. The 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006.[45] E. Zangerle and G. Specht, "Sorry, I was Hacked": a Classification of Compromised Twitter Account. the 29th Annual ACM Symposium on Applied Computing, 2014.[46] Y. Zheng, L. Liu, L. Wang, and X. Xie, Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web. The 17th International Conference on World Wide Web, 2008.[47] 黃懷萱，利用行為脈絡探索社群媒體上的異常使用者，國立政治大學資訊科學系碩士論文，2020。 zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202201618 en_US
