Suspicious Behavior Detection: Current Trends and Future Directions

IEEE Intelligent Systems

Volumn 31, Issue 1, 2016, Pages 31-39

  Jiang, Meng ^a   Cui, Peng ^a   Faloutsos, Christos ^b  

^a TSINGHUA UNIVERSITY   (China)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Behavioral science; Electronic mail; Facebook; Feature extraction; Market research; Support vector machines; Twitter

Indexed keywords

ELECTRONIC MAIL; FEATURE EXTRACTION; SOCIAL NETWORKING (ONLINE); SUPPORT VECTOR MACHINES;

BEHAVIOR DETECTION; BEHAVIORAL PATTERNS; BEHAVIORAL SCIENCE; DETECTION METHODS; FACEBOOK; MARKET RESEARCHES; REAL-WORLD; TWITTER;

BEHAVIORAL RESEARCH;

EID: 84961880408     PISSN: 15411672     EISSN: None     Source Type: Journal    
DOI: 10.1109/MIS.2016.5     Document Type: Article

References (27)

1. C. Xu et al., "An Adaptive Fusion Algorithm for Spam Detection," IEEE Intelligent Systems, vol. 29, no. 4, 2014, pp. 2-8.
2. J. Ratkiewicz et al., "Detecting and Tracking Political Abuse in Social Media," Proc. 5th Int'l AAAI Conf. Weblogs and Social Media, 2011; www. aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2850.
3. K. Lee, J. Caverlee, and S. Webb, "Uncovering Social Spammers: Social Honeypots + Machine Learning," Proc. 33rd Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, 2010, pp. 435-442.
4. P.-A. Chirita, J. Diederich, and W. Nejdl, "Mailrank: Using Ranking for Spam Detection," Proc. 14th ACM Int'l Conf. Information and Knowledge Management, 2005, pp. 373-380.
5. H. Yu et al., "Sybillimit: A Near-Optimal Social Network Defense against Sybil Attacks," IEEE/ACM Trans. Networking, vol. 18, no. 3, 2010, pp. 885-898.
6. J. Ratkiewicz et al., "Truthy: Mapping the Spread of Astroturf in Microblog Streams," Proc. 20th Int'l Conf. Comp. World Wide Web, 2011, pp. 249-252.
7. L. Akoglu, M. McGlohon, and C. Faloutsos, "Oddball: Spotting Anomalies in Weighted Graphs," Advances in Knowledge Discovery and Data Mining, LNCS 6119, Springer, 2010, pp. 410-421.
8. Q. Xu et al., "SMS Spam Detection Using Noncontent Features," IEEE Intelligent Systems, vol. 27, no. 6, 2012, pp. 44-51.
9. A. Beutel et al., "Copycatch: Stopping Group Attacks by Spotting Lockstep Behavior in Social Networks," Proc. 22nd Int'l Conf. World Wide Web, 2013, pp. 119-130.
10. X. Hu et al., "Social Spammer Detection in Microblogging," Proc. 23rd Int'l Joint Conf. Artificial Intelligence, 2013, pp. 2633-2639.
11. Q. Cao et al., "Aiding the Detection of Fake Accounts in Large Scale Social Online Services," Proc. 9th Usenix Conf. Networked Systems Design and Implementation, 2012, pp. 15-28.
12. S. Ghosh et al., "Understanding and Combating Link Farming in the Twitter Social Network," Proc. 21st Int'l Conf. World Wide Web, 2012, pp. 61-70.
13. A. Mukherjee et al., "Spotting Opinion Spammers Using Behavioral Footprints," Proc. 19th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 2013, pp. 632-640.
14. A. Mukherjee, B. Liu, and N. Glance, "Spotting Fake Reviewer Groups in Consumer Reviews," Proc. 21st Int'l Conf. World Wide Web, 2012, pp. 191-200.
15. N. Jindal and B. Liu, "Opinion Spam and Analysis," Proc. 1st Int'l Conf. Web Search and Data Mining, 2008, pp. 219-230.
16. E.-P. Lim et al., "Detecting Product Review Spammers Using Rating Behaviors," Proc. 19th ACM Int'l Conf. Information and Knowledge Management, 2010, pp. 939-948.
17. F. Benevenuto et al., "Detecting Spammers on Twitter," Proc. Collaboration, Electronic Messaging, Anti-Abuse and Spam Conf., vol. 6, 2010, p. 12.
18. H. Gao et al., "Detecting and Characterizing Social Spam Campaigns," Proc. 10th ACM SIGCOMM Conf. Internet Measurement, 2010, pp. 35-47.
19. L. Akoglu, R. Chandy, and C. Faloutsos, "Opinion Fraud Detection in Online Reviews by Network Effects," Proc. Int'l Conf. Web and Social Media, 2013; www.aaai.org/ocs/index. php/ICWSM/ICWSM13/paper/viewFile/5981/6338.
20. M.Araujo et al., "Com2: Fast Automatic Discovery of Temporal ('Comet') Communities," Advances in Knowledge Discovery and Data Mining, LNCS 8444, Springer, 2014, pp. 271-283.
21. M. Jiang et al., "Inferring Strange Behavior from Connectivity Pattern in Social Networks," Advances in Knowledge Discovery and Data Mining, LNCS 8443, Springer, 2014, pp. 126-138.
22. G. Fei et al., "Exploiting Burstiness in Reviews for Review Spammer Detection," Proc. 7th Int'l AAAI Conf. Weblogs and Social Media, 2013; www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/viewFile/6069/6356.
23. M. Jiang et al., "Catchsync: Catching Synchronized Behavior in Large Directed Graphs," Proc. 20th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 2014, pp. 941-950.
24. X. Hu et al., "Social Spammer Detection with Sentiment Information," Proc. Int'l Conf. Data Mining, 2014, pp. 180-189.
25. X. Hu, J. Tang, and H. Liu, "Online Social Spammer Detection," Proc. 28th AAAI Conf. Artificial Intelligence, 2014; www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8467/8399.
26. M. Jiang et al., "FEMA: Flexible Evolutionary Multi-Faceted Analysis for Dynamic Behavioral Pattern Discovery," Proc. 20th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 2014, pp. 1186-1195.
27. M. Jiang et al., "A General Suspiciousness Metric for Dense Blocks in Multi-Modal Data," to be published in Proc. IEEE Int'l Conf. Data Mining, 2015; www.meng-jiang.com/pubs/crossspoticdm15/crossspot-icdm15-paper.pdf.