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http://dx.doi.org/10.6109/jkiice.2020.24.5.567

Exploratory study on the Spam Detection of the Online Social Network based on Graph Properties  

Jeong, Sihyun (Department of Computer and Engineering, Seoul University)
Oh, Hayoung (Global Convergence, Sungkyunkwan University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.5, 2020 , pp. 567-575 More about this Journal
Abstract
As online social networks are used as a critical medium for modern people's information sharing and relationship, their users are increasing rapidly every year. This not only increases usage but also surpasses the existing media in terms of information credibility. Therefore, emerging marketing strategies are deliberately attacking social networks. As a result, public opinion, which should be formed naturally, is artificially formed by online attacks, and many people trust it. Therefore, many studies have been conducted to detect agents attacking online social networks. In this paper, we analyze the trends of researches attempting to detect such online social network attackers, focusing on researches using social network graph characteristics. While the existing content-based techniques may represent classification errors due to privacy infringement and changes in attack strategies, the graph-based method proposes a more robust detection method using attacker patterns.
Keywords
Social Network; Attack detection; Graph property; Pattern analysis; Spam;
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1 J. M. Romo and L. Araujo. "Detecting malicious tweets in trending topics using a statistical analysis of language". Expert Systems with Applications 40.8, 2013
2 S. Y. Schoenebeck, D. M. Romero, G. Schoenebeck, and D. Boyd. "Detecting spam in a twitter network." First Monday, 15(1), January, 2009.
3 M. Egele, G. Stringhini, C. Kruegel, and G. Vigna. "COMPA: Detecting Compromised Accounts on Social Networks." NDSS. 2013.
4 G. Magno, T. Rodrigues, V. Augusto, and F. Almeida. "Detecting spammers on twitter. In Collaboration, Electronic messaging", Anti-Abuse and Spam Conference (CEAS), 2010.
5 X. Li, M. Zhang, Y. Liu, S. Ma, Y. Jin, and L. Ru. "Search engine click spam detection based on bipartite graph propagation." Proceedings of the 7th ACM international conference on Web search and data mining. ACM, 2014.
6 T. Tian, J. Zhu, F. Xia, X. Zhuang, and T. Zhang. "Crowd fraud detection in internet advertising." Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2015.
7 Q. Cao, X. Yang, J. Yu, and C. Palow. "Uncovering large groups of active malicious accounts in online social networks." Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2014.
8 Q. Cao, X. Yang, J. Yu, and C. Palow. "VolTime: Unsupervised Anomaly Detection on Users' Online Activity Volume." Proceedings of the 2017 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2017.
9 L. H. Yu, and D.Y. Yeung. "A learning approach to spam detection based on social networks". Diss. Hong Kong University of Science and Technology, 2007
10 I. Kayes, N. Kourtellis, D. Quercia, A. Iamnitchi, and F.Bonchi. "The social world of content abusers in community question answering." Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2015.
11 E. Zhai, Z. Li, Z. Li, F. Wu, and G. Chen. "Resisting tag spam by leveraging implicit user behaviors." Proceedings of the VLDB Endowment 10.3 (2016): 241-252.
12 H. Zheng, M. Xue, H. Lu, S. Hao, H. Zhu, X. Liang, and K. W. Ross "Smoke screener or straight shooter: Detecting elite sybil attacks in user-review social networks." arXiv preprint arXiv:1709.06916 (2017).
13 L. Akoglu, M. McGlohon, and C. Faloutsos. "Oddball: Spotting anomalies in weighted graphs." Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2010. 410-421.
14 M. Jiang, P. Cui, A. Beutel, C. Faloutsos, and S. Yang "Inferring strange behavior from connectivity pattern in social networks." Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2014. 126-138.
15 M. Jiang, P. Cui, A. Beutel, C. Faloutsos, and S. Yang. "CatchSync: catching synchronized behavior in large directed graphs." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.
16 M. Newman,. Networks. Oxford university press, 2018.
17 D. O'Callaghan, M. Harrigan, J. Carthy, and P. Cunningham." Network Analysis of Recurring YouTube Spam Campaigns." ICWSM. 2012.
18 S. Jeong, G. Noh, H. Oh, and C. Kim. "Follow spam detection based on cascaded social information." Information Sciences 369 (2016): 481-499.   DOI
19 N. Z. Gong, M. Frank, and P. Mittal. "SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection." Information Forensics and Security, IEEE Transactions on 9.6 (2014): 976-987.   DOI
20 S. Ghosh, B. Viswanath, F. Kooti, N. K. Sharma, G. Korlam, F. Benevenuto, N. Ganguly, and K. P. Gummadi. "Understanding and combating link farming in the twitter social network." Proceedings of the 21st international conference on World Wide Web. ACM, 2012.
21 D. Yuan, G. Li, Q. Li, and Y. Zheng. "Sybil defense in crowdsourcing platforms." Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017.
22 B. Viswanath, M. A. Bashir, M. Crovella, S. Guha, K. P. Gummadi, B. Krishnamurthy, and A. Mislove. "Towards detecting anomalous user behavior in online social networks." Proceedings of the 23rd USENIX Security Symposium (USENIX Security). 2014.
23 N. Shah, A. Beutel, B. Gallagher, and C. Faloutsos. "Spotting suspicious link behavior with fBox: an adversarial perspective." Data Mining (ICDM), 2014 IEEE International Conference on. IEEE, 2014.
24 A. Beutel, K. Murray, C. Faloutsos, and A. J. Smola. "Cobafi: collaborative bayesian filtering." Proceedings of the 23rd international conference on World wide web. ACM, 2014.
25 M. Gupta, J. Gao, and J. Han. "Community distribution outlier detection in heterogeneous information networks." Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2013. 557-573.
26 I. Kayes, N. Kourtellis, D. Quercia, A. Iamnitchi, and F. Bonchi. "The Social World of Content Abusers in Community Question Answering." Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2015.
27 B. Perozzi, and L. Akoglu. "Scalable anomaly ranking of attributed neighborhoods." Proceedings of the 2016 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2016.
28 S. Dhawan, S. C. R. Gangireddy, S. Kumar, and T. Chakraborty. 2019. "Spotting collective behaviour of online frauds in customer reviews." In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Sarit Kraus (Ed.). AAAI Press 245-251.
29 S. Jeong, J. Lee, J. Park, and C. Kim. "The Social Relation Key: A new paradigm for security." Information Systems 71 (2017): 68-77.   DOI
30 M. McPherson, L. S. Lovin, and J. M. Cook. "Birds of a feather: Homophily in social networks." Annual review of sociology 27.1 (2001): 415-444.   DOI
31 R. Milo, S. Itzkovitz, N. Kashtan, R. Levitt, S. S. Orr, I. Ayzenshtat, M. Sheffer, and U. Alon. "Superfamilies of evolved and designed networks." Science 303.5663 (2004): 1538-1542.   DOI
32 O. N. Yaveroglu, N. M. Dognin, D. Davis, Z. Levnajic, V. Janjic, R. Karapandza, A. Stojmirovic, and N. Przulj. "Revealing the hidden language of complex networks." Scientific reports 4 (2014).
33 Y. Koren, "Collaborative filtering with temporal dynamics." Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009.
34 R. Salakhutdinov, and A. Mnih. "Bayesian probabilistic matrix factorization using Markov chain Monte Carlo." Proceedings of the 25th international conference on Machine learning. ACM, 2008.
35 L.Page, S. Brin, R. Motwani, and T. Winograd. "The PageRank citation ranking: Bringing order to the web." Stanford InfoLab, 1999.
36 J. Kleinberg, "Hubs, authorities, and communities." ACM computing surveys (CSUR) 31.4es (1999): 5.   DOI
37 Lee Chan-chan, Seo Go-eun, Shin-yong Shin, Dong-gun Kim, & Jae-hee Cho. (2015). "Improved tweet bot detection using geographic space and device information." Journal of the Korea Information and Communication Society, 19(12), 2878-2884.   DOI