Knowledge‐based approaches for identity management in online social networks

Advanced Review

Leila Bahri, Barbara Carminati, Elena Ferrari

Published Online: Apr 30 2018

DOI: 10.1002/widm.1260

Full article on Wiley Online Library: HTML PDF

Can't access this content? Tell your librarian.

When we meet a new person, we start by introducing ourselves. We share our names, and other information about our jobs, cities, family status, and so on. This is how socializing and social interactions can start: we first need to identify each other. Identification is a cornerstone in establishing social contacts. We identify ourselves and others by a set of civil (e.g., name, nationality, ID number, gender) and social (e.g., music taste, hobbies, religion) characteristics. This seamlessly carried out identification process in face‐to‐face interactions is challenged in the virtual realms of socializing, such as in online social network (OSN) platforms. New identities (i.e., online profiles) could be created without being subject to any level of verification, making it easy to create fake information and forge fake identities. This has led to a massive proliferation of accounts that represent fake identities (i.e., not mapping to physically existing entities), and that poison the online socializing environment with fake information and malicious behavior (e.g., child abuse, information stealing). Within this milieu, users in OSNs are left unarmed against the challenging task of identifying the real person behind the screen. OSN providers and research bodies have dedicated considerable effort to the study of the behavior and features of fake OSN identities, trying to find ways to detect them. Some other research initiatives have explored possible techniques to enable identity validation in OSNs. Both kinds of approach rely on extracting knowledge from the OSN, and exploiting it to achieve identification management in their realms. We provide a review of the most prominent works in the literature. We define the problem, provide a taxonomy of related attacks, and discuss the available solutions and approaches for knowledge‐based identity management in OSNs. This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Application Areas> Internet and Web‐Based Applications Application Areas> Society and Culture

A high‐level taxonomy of identity‐related attacks in online social networks

[ Normal View | Magnified View ]

A sybil attack on sample online social network graph, visualizing the sybil (S) and honest (H) regions, with attack links between them in red color

[ Normal View | Magnified View ]

An overview of the spectrum of available identity attack defense mechanisms categorized by the purpose they serve. (Reprinted with permission from Bahri et al. (). Copyright ACM, 2016)

[ Normal View | Magnified View ]

References

Alvisi,, L., Clement,, A., Epasto,, A., Lattanzi,, S., & Panconesi,, A. (2013). Sok: The evolution of sybil defense via social networks. Paper presented at the Proceeding of the 2013 I.E. Symposium on Security and Privacy (SP), Berkeley, CA, USA (pp. 382–396).
Bahri,, L., Carminati,, B., & Ferrari,, E. (2014). Community‐based identity validation in online social networks. Paper presented at the Proceedings of the 34th International Conference on Distributed Computing Systems, Madrid, Spain.
Bahri,, L., Carminati,, B., & Ferrari,, E. (2016). Coip: Continuous, operable, impartial, and privacy‐aware identity validity estimation for OSN profiles. ACM Transactions on the Web (TWEB), 10(4), 23.
Bara,, I.‐A., Fung,, C. J., & Dinh,, T. (2015). Enhancing twitter spam accounts discovery using cross‐account pattern mining. Paper presented at the Proceedings of 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), Ottawa, ON, Canada (pp. 491–496).
Bilge,, L., Strufe,, T., Balzarotti,, D., & Kirda,, E. (2009). All your contacts are belong to us: Automated identity theft attacks on social networks. Paper presented at the Proceedings of the 18th International Conference on World Wide Web, Madrid, Spain (pp. 551–560).
Boshmaf,, Y., Beznosov,, K., & Ripeanu,, M. (2013). Graph‐based sybil detection in social and information systems. Paper presented at the Proceedings of 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Niagara, Ontario, Canada.
Cai,, X., Bain,, M., Krzywicki,, A., Wobcke,, W., Sok Kim,, Y., Compton,, P., & Mahidadia,, A. (2011). Collaborative filtering for people to people recommendation in social networks. In AI 2010: Advances in Artificial Intelligence. Springer, 476–485.
Cao,, Q., Sirivianos,, M., Yang,, X., & Pregueiro,, T. (2012). Aiding the detection of fake accounts in large scale social online services. Paper presented at the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12), San Jose, CA, USA (pp. 197–210).
Cao,, Q., Yang,, X., Yu,, J., & Palow,, C. (2014). Uncovering large groups of active malicious accounts in online social networks. Paper presented at the Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Scottsdale, Arizona, USA (pp. 477–488).
Conti,, M., Poovendran,, R., & Secchiero,, M. (2012). Fakebook: Detecting fake profiles in on‐line social networks. Paper presented at the Proceedings of 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Istanbul, Turkey (pp. 1071–1078).
Danezis,, G., & Mittal,, P. (2009). Sybilinfer: Detecting sybil nodes using social networks. Paper presented at the Network and Distributed System Security Symposium, NDSS, San Diego, CA, USA.
Dave,, D., Mishra,, N., Sharma,, S., Jin,, L., Long,, X., & Takabi,, H. (2013). Detection techniques of clone attack on online social networks: Survey and analysis. Elsevier.
Douceur,, J. R. (2002). The sybil attack. Paper presented at the International Workshop on Peer‐to‐Peer Systems, Cambridge, MA, USA (pp. 251–260).
Egele,, M., Stringhini,, G., Kruegel,, C., & Vigna,, G. (2013). Compa: Detecting compromised accounts on social networks. Paper presented at the The Network and Distributed System Security Symposium, NDSS, San Diego, CA, USA.
Egele,, M., Stringhini,, G., Kruegel,, C., & Vigna,, G. (2015). Towards detecting compromised accounts on social networks. IEEE Transactions on Dependable and Secure Computing, 14, 447–460.
Fire,, M., Goldschmidt,, R., & Elovici,, Y. (2014). Online social networks: Threats and solutions. IEEE Communications Surveys %26 Tutorials, 16(4), 2019–2036.
Gong,, N. Z., Frank,, M., & Mittal,, P. (2014). Sybilbelief: A semi‐supervised learning approach for structure‐based sybil detection. IEEE Transactions on Information Forensics and Security, 9(6), 976–987.
He,, B.‐Z., Chen,, C.‐M., Su,, Y.‐P., & Sun,, H.‐M. (2014). A defence scheme against identity theft attack based on multiple social networks. Expert Systems with Applications, 41(5), 2345–2352.
Jiang,, J., Shan,, Z.‐F., Wang,, X., Zhang,, L., & Dai,, Y.‐F. (2015). Understanding sybil groups in the wild. Journal of Computer Science and Technology, 30(6), 1344–1357.
Jiang,, M., Cui,, P., Beutel,, A., Faloutsos,, C., & Yang,, S. (2014). Catchsync: Catching synchronized behavior in large directed graphs. Paper presented at the Proceedings of the 20th CM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA (pp. 941–950).
Jin,, L., Chen,, Y., Wang,, T., Hui,, P., & Vasilakos,, A. V. (2013). Understanding user behavior in online social networks: A survey. IEEE Communications Magazine, 51(9), 144–150.
Jin,, L., Joshi,, J. B., & Anwar,, M. (2013). Mutual‐friend based attacks in social network systems. Computers %26 Security, 37, 15–30.
Jin,, L., Takabi,, H., & Joshi,, J. B. (2011). Towards active detection of identity clone attacks on online social networks. Paper presented at the Proceedings of the First ACM Conference on Data and Application Security and Privacy, San Antonio, TX, USA (pp. 27–38).
Kansara,, K. B., & Shekokar,, N. M. (2015). At a glance of sybil detection in OSN. Paper presented at the Proceedings of 2015 I.E. International Symposium on Nanoelectronic and Information Systems, Indore, India (pp. 47–52).
Laleh,, N., Carminati,, B., & Ferrari,, E. (2015). Graph based local risk estimation in large scale online social networks. Paper presented at the Proceedings of 2015 I.E. International Conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, China (pp. 528–535).
Li,, Y., Martinez,, O., Chen,, X., Li,, Y., & Hopcroft,, J. E. (2016). In a world that counts: Clustering and detecting fake social engagement at scale. Paper presented at the Proceedings of the 25th International Conference on World Wide Web, Montréal, Québec, Canada (pp. 111–120).
Mitchell,, K. J., Finkelhor,, D., Jones,, L. M., & Wolak,, J. (2010). Use of social networking sites in online sex crimes against minors: An examination of national incidence and means of utilization. Journal of Adolescent Health, 47(2), 183–190.
Reyns,, B. W., & Henson,, B. (2016). The thief with a thousand faces and the victim with none: Identifying determinants for online identity theft victimization with routine activity theory. International Journal of Offender Therapy and Comparative Criminology, 60(10), 1119–1139.
Sirivianos,, M., Kim,, K., Jian,, W. G., & Yang,, X. (2014). Leveraging social feedback to verify online identity claims. ACM Trans. Web 8, 2(2014), 9.
Soliman,, A., Bahri,, L., Carminati,, B., Ferrari,, E., & Girdzijauskas,, S. (2015). Diva: Decentralized identity validation for social networks. Paper presented at the Proceedings of 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, France (ASONAM).
Soliman,, A., Bahri,, L., Girdzijauskas,, S., Carminati,, B., & Ferrari,, E. (2016). Cadiva: Cooperative and adaptive decentralized identity validation model for social networks. Social Network Analysis and Mining, 6(1), 1–22.
Tran,, N., Li,, J., Subramanian,, L., & Chow,, S. S. (2011). Optimal sybil‐resilient node admission control. Paper presented at the Proceedings of IEEE INFOCOM, 2011, Shanghai, China (pp. 3218–3226).
Villar‐Rodíguez,, E., Del Ser,, J., Torre‐Bastida,, A. I., Bilbao,, M. N., & Salcedo‐Sanz,, S. (2016). A novel machine learning approach to the detection of identity theft in social networks based on emulated attack instances and support vector machines. Concurrency and Computation: Practice and Experience, 28(4), 1385–1395.
Wang,, C., Yang,, B., & Luo,, J. (2017). Identity theft detection in mobile social networks using behavioral semantics. Paper presented at the Proceedings of 2017 I.E. International Conference on Smart Computing (SMARTCOMP), Hong Kong, China (pp. 1–3).
Wang,, G., Konolige,, T., Wilson,, C., Wang,, X., Zheng,, H., & Zhao,, B. Y. (2013). You are how you click: Clickstream analysis for sybil detection. Paper presented at the Proceedings of 2012 USENIX Security, Washington, D.C., USA (pp. 1–15).
Wang,, G., Mohanlal,, M., Wilson,, C., Wang,, X., Metzger,, M., Zheng,, H., & Zhao,, B. Y. (2012). Social turing tests: Crowdsourcing sybil detection. arXiv:1205.3856.
Wilson,, D. (2017). Surveillance, crime and social control. CRC Press, LLC: Routledge.
Xiao,, C., Freeman,, D. M., & Hwa,, T. (2015). Detecting clusters of fake accounts in online social networks. Paper presented at the Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security, Denver, Colorado, USA (pp. 91–101).
Yang,, Z., Wilson,, C., Wang,, X., Gao,, T., Zhao,, B. Y., & Dai,, Y. (2014). Uncovering social network sybils in the wild. ACM Transactions on Knowledge Discovery from Data (TKDD), 8(1), 2.
Yu,, H., Gibbons,, P. B., Kaminsky,, M., & Xiao,, F. (2008). Sybillimit: A near‐optimal social network defense against sybil attacks. Paper presented at the Proceedings of 2008 I.E. Symposium on Security and Privacy (SP), Oakland, CA, USA (pp. 3–17).
Yu,, H., Kaminsky,, M., Gibbons,, P. B., & Flaxman,, A. (2006). Sybilguard: Defending against sybil attacks via social networks. ACM SIGCOMM Computer Communication Review, 36, 267–278.
Zangerle,, E., & Specht,, G. (2014). Sorry, i was hacked: A classification of compromised twitter accounts. Paper presented at the Proceedings of the 29th Annual ACM Symposium on Applied Computing, Gyeongju, Republic of Korea (pp. 587–593).
Zhang,, K., Liang,, X., Lu,, R., & Shen,, X. (2014). Sybil attacks and their defenses in the internet of things. Internet of Things Journal, IEEE, 1(5), 372–383.

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

Knowledge‐based approaches for identity management in online social networks

Related Articles

Browse by Topic

Access to this WIREs title is by subscription only.

The latest WIREs articles in your inbox