Man vs. Machine: Practical Adversarial Detection of Malicious Crowdsourcing Workers
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[1] Gang Wang,et al. Serf and turf: crowdturfing for fun and profit , 2011, WWW.
[2] Chao Yang,et al. Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers , 2011, IEEE Transactions on Information Forensics and Security.
[3] Albert G. Greenberg,et al. Network anomography , 2005, IMC '05.
[4] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[5] Santosh S. Vempala,et al. Filtering spam with behavioral blacklisting , 2007, CCS '07.
[6] Gang Wang,et al. Northeastern University , 2021, IEEE Pulse.
[7] Pat Langley,et al. Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.
[8] Gang Wang,et al. Social Turing Tests: Crowdsourcing Sybil Detection , 2012, NDSS.
[9] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[10] Rich Caruana,et al. An empirical evaluation of supervised learning in high dimensions , 2008, ICML '08.
[11] Dawn Xiaodong Song,et al. Suspended accounts in retrospect: an analysis of twitter spam , 2011, IMC '11.
[12] Vern Paxson,et al. Outside the Closed World: On Using Machine Learning for Network Intrusion Detection , 2010, 2010 IEEE Symposium on Security and Privacy.
[13] James Newsome,et al. Polygraph: automatically generating signatures for polymorphic worms , 2005, 2005 IEEE Symposium on Security and Privacy (S&P'05).
[14] Richard E. Neapolitan,et al. Learning Bayesian networks , 2007, KDD '07.
[15] Gary Robinson,et al. A statistical approach to the spam problem , 2003 .
[16] Claire Cardie,et al. Finding Deceptive Opinion Spam by Any Stretch of the Imagination , 2011, ACL.
[17] Blaine Nelson,et al. Poisoning Attacks against Support Vector Machines , 2012, ICML.
[18] Ling Huang,et al. Query Strategies for Evading Convex-Inducing Classifiers , 2010, J. Mach. Learn. Res..
[19] Mark Crovella,et al. Diagnosing network-wide traffic anomalies , 2004, SIGCOMM '04.
[20] Jun Hu,et al. Detecting and characterizing social spam campaigns , 2010, CCS '10.
[21] Kyumin Lee,et al. The Dark Side of Micro-Task Marketplaces: Characterizing Fiverr and Automatically Detecting Crowdturfing , 2014, ICWSM.
[22] Steven Gianvecchio,et al. Detecting covert timing channels: an entropy-based approach , 2007, CCS '07.
[23] Dawn Xiaodong Song,et al. Limits of Learning-based Signature Generation with Adversaries , 2008, NDSS.
[24] Virgílio A. F. Almeida,et al. Detecting Spammers on Twitter , 2010 .
[25] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.
[26] Gang Wang,et al. Wisdom in the social crowd: an analysis of quora , 2013, WWW.
[27] Ling Huang,et al. ANTIDOTE: understanding and defending against poisoning of anomaly detectors , 2009, IMC '09.
[28] Pavel Laskov,et al. Practical Evasion of a Learning-Based Classifier: A Case Study , 2014, 2014 IEEE Symposium on Security and Privacy.
[29] Ling Huang,et al. Approaches to adversarial drift , 2013, AISec.
[30] Srinivasan Venkatesh,et al. The best answers? Think twice: Online detection of commercial campaigns in the CQA forums , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).
[31] Stefan Savage,et al. Dirty Jobs: The Role of Freelance Labor in Web Service Abuse , 2011, USENIX Security Symposium.
[32] Pedro M. Domingos,et al. Adversarial classification , 2004, KDD.
[34] Fabrício Benevenuto,et al. Reverse engineering socialbot infiltration strategies in Twitter , 2014, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).
[35] Liang Zhang,et al. Online modeling of proactive moderation system for auction fraud detection , 2012, WWW.
[36] Gianluca Stringhini,et al. Detecting spammers on social networks , 2010, ACSAC '10.
[37] Ben Y. Zhao,et al. Uncovering social network Sybils in the wild , 2011, ACM Trans. Knowl. Discov. Data.
[38] Giorgio Giacinto,et al. Looking at the bag is not enough to find the bomb: an evasion of structural methods for malicious PDF files detection , 2013, ASIA CCS '13.
[39] Yiming Yang,et al. A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.
[40] Fabio Roli,et al. Evasion Attacks against Machine Learning at Test Time , 2013, ECML/PKDD.
[41] Ben Y. Zhao,et al. Beyond Social Graphs: User Interactions in Online Social Networks and their Implications , 2012, TWEB.
[42] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[43] Seungyeop Han,et al. Analysis of topological characteristics of huge online social networking services , 2007, WWW '07.
[44] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[45] Ling Huang,et al. Near-Optimal Evasion of Convex-Inducing Classifiers , 2010, AISTATS.
[46] Sushil Jajodia,et al. Who is tweeting on Twitter: human, bot, or cyborg? , 2010, ACSAC '10.
[47] J. Doug Tygar,et al. Adversarial machine learning , 2019, AISec '11.
[48] Kyumin Lee,et al. Crowdturfers, Campaigns, and Social Media: Tracking and Revealing Crowdsourced Manipulation of Social Media , 2013, ICWSM.