Social Fingerprinting: Detection of Spambot Groups Through DNA-Inspired Behavioral Modeling

IEEE Transactions on Dependable and Secure Computing

Volumn 15, Issue 4, 2018, Pages 561-576

  Cresci, Stefano ^a   Pietro, Roberto Di ^a,b,c   Petrocchi, Marinella ^a   Spognardi, Angelo ^a,d   Tesconi, Maurizio ^a  

^a CNR   (Italy)

^b LUCENT TECHNOLOGIES   (United States)

^c UNIVERSITY OF PADOVA   (Italy)

^d TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

behavioral modeling; digital DNA; online social networks; social bots; Spambot detection; Twitter

Indexed keywords

BEHAVIORAL RESEARCH; DNA; DNA SEQUENCES; SOCIAL NETWORKING (ONLINE);

BEHAVIORAL MODEL; COLLECTIVE BEHAVIOR; FINGERPRINTING TECHNIQUES; ON-LINE SOCIAL NETWORKS; SIMILARITY MEASURE; SOCIAL BOTS; STATE-OF-THE-ART ALGORITHMS; TWITTER;

PALMPRINT RECOGNITION;

EID: 85049772465     PISSN: 15455971     EISSN: 19410018     Source Type: Journal    
DOI: 10.1109/TDSC.2017.2681672     Document Type: Article

References (56)

1. H. Liu, J. Han, and H. Motoda, "Uncovering deception in social media, " Social Netw. Anal. Mining, vol. 4, no. 1, pp. 1-2, 2014.
2. G. Stringhini, M. Egele, C. Kruegel, and G. Vigna, "Poultry markets: On the underground economy of Twitter followers, " in Proc. ACM Workshop Workshop Online Social Netw., 2012, pp. 1-6.
3. G. Stringhini, et al., "Follow the green: Growth and dynamics in Twitter follower markets, " in Proc. 13th ACM SIGCOMM Internet Meas. Conf., 2013, pp. 163-176.
4. K. Thomas, D. McCoy, C. Grier, A. Kolcz, and V. Paxson, "Trafficking fraudulent accounts: The role of the underground market in Twitter spam and abuse, " in Proc. 22nd USENIX Secur. Symp., 2013, pp. 195-210.
5. K. Lee, J. Caverlee, and S. Webb, "Uncovering social spammers: Social honeypots + machine learning, " in Proc. 33rd Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2010, pp. 435-442.
6. G. Stringhini, C. Kruegel, and G. Vigna, "Detecting spammers on social networks, " in Proc. 26th Annu. Comput. Secur. Appl. Conf., 2010, pp. 1-9.
7. S. Fortunato, "Community detection in graphs, " Phys. Rep., vol. 486, no. 3, pp. 75-174, 2010.
8. S. Ghosh, et al., "Understanding and combating link farming in the Twitter social network, " in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 61-70.
9. C. Yang, R. Harkreader, and G. Gu, "Empirical evaluation and new design for fighting evolving Twitter spammers, " IEEE Trans. Inf. Forensics Secur., vol. 8, no. 8, pp. 1280-1293, Aug. 2013.
10. X. Hu, J. Tang, and H. Liu, "Online social spammer detection, " in Proc. 28th AAAI Conf. Artif. Intell., 2014, pp. 59-65.
11. E. Ferrara, O. Varol, C. Davis, F. Menczer, and A. Flammini, "The rise of social bots, " Commun. ACM, vol. 59, no. 7, pp. 96-104, 2016.
12. M. Jiang, P. Cui, A. Beutel, C. Faloutsos, and S. Yang, "Catching synchronized behaviors in large networks: A graph mining approach, " ACM Trans. Knowl. Discovery Data, vol. 10, no. 4, 2016, Art. no. 35.
13. K. Li and Y. Fu, "Prediction of human activity by discovering temporal sequence patterns, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 8, pp. 1644-1657, Aug. 2014.
14. Y. Boshmaf, I. Muslukhov, K. Beznosov, and M. Ripeanu, "The socialbot network: When bots socialize for fame and money, " in Proc. 27th Annu. Comput. Secur. Appl. Conf., 2011, pp. 93-102.
15. Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia, "Detecting automation of Twitter accounts: Are you a human, bot, or cyborg?" IEEE Trans. Depend. Secure Comput., vol. 9, no. 6, pp. 811-824, Nov./Dec. 2012.
16. S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi, "Fame for sale: Efficient detection of fake Twitter followers, " Decision Support Syst., vol. 80, pp. 56-71, 2015.
17. S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi, "A criticism to society (as seen by Twitter analytics), " in Proc. IEEE 1st Int. Workshop Big Data Anal. Secur., Jun. 2014, pp. 194-200.
18. H. Gao, Y. Chen, K. Lee, D. Palsetia, and A. N. Choudhary, "Towards online spam filtering in social networks, " in Proc. Netw. Distrib. Syst. Secur. Symp., 2012, pp. 1-16.
19. S. Lee and J. Kim, "WarningBird: A near real-time detection system for suspicious URLs in Twitter stream, " IEEE Trans. Depend. Secure Comput., vol. 10, no. 3, pp. 183-195, May/Jun. 2013.
20. K. Thomas, C. Grier, J. Ma, V. Paxson, and D. Song, "Design and evaluation of a real-time URL spam filtering service, " in Proc. IEEE 32nd Symp. Secur. Privacy, 2011, pp. 447-462.
21. H. Gao, et al., "Spam ain't as diverse as it seems: Throttling OSN spam with templates underneath, " in Proc. 30th Annu. Comput. Secur. Appl. Conf., 2014, pp. 76-85.
22. Y. Liu, B. Wu, B. Wang, and G. Li, "SDHM: A hybrid model for spammer detection in Weibo, " in Proc. IEEE/ACM Int. Conf. Advances Social Netw. Anal. Mining, 2014, pp. 942-947.
23. Z. Yang, C. Wilson, X. Wang, T. Gao, B. Y. Zhao, and Y. Dai, "Uncovering social network sybils in the wild, " ACM Trans. Knowl. Discovery Data, vol. 8, no. 1, 2014, Art. no. 2.
24. A. Beutel, W. Xu, V. Guruswami, C. Palow, and C. Faloutsos, "CopyCatch: stopping group attacks by spotting lockstep behavior in social networks, " in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 119-130.
25. M. Jiang, P. Cui, A. Beutel, C. Faloutsos, and S. Yang, "Inferring lockstep behavior from connectivity pattern in large graphs, " Knowl. Inf. Syst., vol. 48, no. 2, pp. 399-428, 2015.
26. M. Giatsoglou, D. Chatzakou, N. Shah, A. Beutel, C. Faloutsos, and A. Vakali, "ND-Sync: Detecting synchronized fraud activities, " in Proc. 19th Pacific Asia Conf. Advances Knowl. Discovery Data Mining, 2015, pp. 201-214.
27. Q. Cao, X. Yang, J. Yu, and C. Palow, "Uncovering large groups of active malicious accounts in online social networks, " in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., 2014, pp. 477-488.
28. R. Yu, X. He, and Y. Liu, "GLAD: Group anomaly detection in social media analysis, " ACM Trans. Knowl. Discovery Data, vol. 10, no. 2, pp. 1-22, 2015.
29. M. Ou, P. Cui, J. Wang, F. Wang, and W. Zhu, "Probabilistic attributed hashing, " in Proc. AAAI Conf. Artif. Intell., 2015, pp. 2894-2900.
30. F. Ahmed and M. Abulaish, "A generic statistical approach for spam detection in online social networks, " Comput. Commun., vol. 36, no. 10, pp. 1120-1129, 2013.
31. Z. Miller, B. Dickinson, W. Deitrick, W. Hu, and A. H. Wang, "Twitter spammer detection using data stream clustering, " Inf. Sci., vol. 260, pp. 64-73, 2014.
32. S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi, "DNA-inspired online behavioral modeling and its application to spambot detection, " IEEE Intell. Syst., vol. 31, no. 5, pp. 58-64, Sep./Oct. 2016.
33. K. L. Gwet, Handbook of Inter-Rater Reliability: The Definitive Guide to Measuring the Extent of Agreement Among Raters. Gaithersburg, MD, USA: Advanced Analytics, LLC, 2014.
34. J. R. Landis and G. G. Koch, "The measurement of observer agreement for categorical data, " Biometrics, vol. 33, pp. 159-174, 1977.
35. M. Avvenuti, S. Bellomo, S. Cresci, M. N. La Polla, and M. Tesconi, "Hybrid crowdsensing: A novel paradigm to combine the strengths of opportunistic and participatory crowdsensing, " in Proc. 26th World Wide Web Conf., 2017.
36. R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction. Cambridge, U.K.: Cambridge Univ. Press, 2014.
37. B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, and M. Demi-rbas, "Short text classification in Twitter to improve information filtering, " in Proc. 33rd Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2010, pp. 841-842.
38. K. Lee, D. Palsetia, R. Narayanan, M. M. A. Patwary, A. Agrawal, and A. Choudhary, "Twitter trending topic classification, " in Proc. IEEE 11th Int. Conf. Data Mining Workshops, 2011, pp. 251-258.
39. D. Gusfield, Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Cambridge, U.K.: Cambridge Univ. Press, 1997.
40. M. Arnold and E. Ohlebusch, "Linear time algorithms for generalizations of the longest common substring problem, " Algorithmica, vol. 60, no. 4, pp. 806-818, 2011.
41. L. Chi and K. Hui, "Color set size problem with applications to string matching, " in Combinatorial Pattern Matching. Berlin, Germany: Springer, 1992, pp. 230-243.
42. T. Fawcett, "An introduction to ROC analysis, " Pattern Recognit. Lett., vol. 27, no. 8, pp. 861-874, 2006.
43. P. Baldi, S. Brunak, Y. Chauvin, and H. Nielsen, "Assessing the accuracy of prediction algorithms for classification: An overview, " Bioinf., vol. 16, no. 5, pp. 412-424, 2000.
44. S. J. Pan and Q. Yang, "A survey on transfer learning, " IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.
45. G. Palshikar, "Simple algorithms for peak detection in time-series, " in Proc. 1st Int. Conf. Adv. Data Anal. Business Anal. Intell., 2009, pp. 1-13.
46. V. Lampos and N. Cristianini, "Nowcasting events from the social web with statistical learning, " ACM Trans. Intell. Syst. Technol., vol. 3, no. 4, 2012, Art. no. 72.
47. Twitter Inc., "Twitter's IPO filing, " Oct 2013. [Online]. Available: https://goo.gl/pbXxHh, Last checked Dec. 07, 2016.
48. R. Kohavi and F. Provost, "Glossary of terms, " Mach. Learn., vol. 30, no. 2/3, pp. 271-274, 1998.
49. D. M. W. Powers, "Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation, " Int. J. Mach. Learn. Technol., vol. 2, no. 1, pp. 37-63, 2011.
50. Y. Chen, A. Wan, and W. Liu, "A fast parallel algorithm for finding the longest common sequence of multiple biosequences, " BMC Bioinf., vol. 7, no. 4, 2006, Art. no. 1.
51. F. Benevenuto, T. Rodrigues, M. Cha, and V. Almeida, "Characterizing user navigation and interactions in online social networks, " Inf. Sci., vol. 195, pp. 1-24, 2012.
52. B. Viswanath, et al., "Towards detecting anomalous user behavior in online social networks, " in Proc. 23rd USENIX Secur. Symp., 2014, pp. 223-238.
53. X. Ruan, Z. Wu, H. Wang, and S. Jajodia, "Profiling online social behaviors for compromised account detection, " IEEE Trans. Inf. Forensics Secur., vol. 11, no. 1, pp. 176-187, Jan. 2016.
54. L. Cao, "In-depth behavior understanding and use: The behavior informatics approach, " Inf. Sci., vol. 180, no. 17, pp. 3067-3085, 2010.
55. D. P. Kroese, T. Brereton, T. Taimre, and Z. I. Botev, "Why the Monte Carlo method is so important today, " Wiley Interdisciplinary Rev.: Computat. Statist., vol. 6, no. 6, pp. 386-392, 2014.
56. L. Bergroth, H. Hakonen, and T. Raita, "A survey of longest common subsequence algorithms, " in Proc. 7th Int. Symp. String Process. Inf. Retrieval, 2000, pp. 39-48.