Investigation of User Vulnerability in Social Networking Site

  • Dalius MažeikaEmail author
  • Jevgenij Mikejan
Part of the Studies in Computational Intelligence book series (SCI, volume 869)


The vulnerability of the social network users becomes a social networking problem. A single vulnerable user might place all friends at risk therefore, it is important to know how the security of the social network users can be improved. In this research, we aim to address issues related to user vulnerability to a phishing attack. Short text messages of the social network site users were gathered, cleaned and analyzed. Moreover, phishing messages were build using social engineering methods and sent to the users. K-means and Mini Batch K-means clustering algorithm were evaluated for the user clustering based on their text messages. A special tool was developed to automate the users clustering process and a phishing attack. Analysis of users responses to the phishing messages built using different datasets and social engineering methods was performed, and corresponding conclusions about user vulnerability were made.


User vulnerability Phishing attack Social network 


  1. Acquisti A, Gross R (2009) Predicting social security numbers from public data. Proc Natl Acad Sci 106(27):10975–10980CrossRefGoogle Scholar
  2. Arnaboldi V, Guazzini A, Passarella A (2013) Egocentric online social networks: analysis of key features and prediction of tie strength in Facebook. Comput Commun 36:1130–1144CrossRefGoogle Scholar
  3. Buglass SL, Binder JF, Betts LR, Underwood JDM (2016) When ‘friends’ collide: social heterogeneity and user vulnerability on social network sites. Comput Hum Behav 54:62–72CrossRefGoogle Scholar
  4. Bullée JWH, Montoya L, Pieters W, Junger M, Hartel P (2018) On the anatomy of social engineering attacks—a literature-based dissection of successful attacks. J Investig Psychol Offender Profiling 15:20–45Google Scholar
  5. Burger JD, Henderson J, Kim G, Zarrella G (2011) Discriminating gender on twitter. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 1301–1309Google Scholar
  6. Burke M, Kraut RE (2014) Growing closer on facebook: changes in tie strength through social network site use. In: Proceeding of the SIGCHI conference on human factors in computing systems, pp 4187–4196Google Scholar
  7. Celebi ME, Kingravi HA, Vela PA (2013) A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst Appl 40(1):200–210CrossRefGoogle Scholar
  8. Conti M, Passarella A, Pezzoni F (2012) From ego network to social network models. In: Proceedings of the third ACM international workshop on mobile opportunistic networks, MobiOpp’12. ACM, New York, pp 91–92Google Scholar
  9. Duda R, Hart PE, Stork DG (1996) kPattern classification and scene analysis. Wiley, New YorkGoogle Scholar
  10. Dwyer C, Hiltz SR, Passerini K (2007) Trust and privacy concern within social networking sites: a comparison of Facebook and MySpace. In: AMCIS 2007 proceedings, p 339Google Scholar
  11. Gundecha P, Barbier G, Tang J, Liu H (2014) User vulnerability and its reduction on a social networking site. ACM Trans Knowl Discov Data 9(2). Article 12Google Scholar
  12. Ivaturi K, Janczewski L (2011) A taxonomy for social engineering attacks. In: Proceedings of international conference on information resources CONF-IRM 2011 proceedings, p 15Google Scholar
  13. Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Proc Natl Acad SciGoogle Scholar
  14. Krishnamurthy B, Wills C (2009) On the leakage of personally identifiable information via online social networks. ACM SIGCOMM Comput Commun Rev 40(1):112–117CrossRefGoogle Scholar
  15. Krombholz K, Hobel H, Huber M, Weippl E (2015) Advanced social engineering attacks. J Inf Secur Appl 22:113–122Google Scholar
  16. Liu H, Maes P (2005) Interestmap: harvesting social network profiles for recommendations. Beyond Personalization IUI-2005, USAGoogle Scholar
  17. Mansour RF (2016) Understanding how big data leads to social networking vulnerability. Comput Hum Behav 57:348–351CrossRefGoogle Scholar
  18. Narayanan A, Shmatikov V (2009) De-anonymizing social networks. In: 30th IEEE symposium on security and privacyGoogle Scholar
  19. Number of social media users worldwide from 2010 to 2021. Available at: Last accessed 25.03.2019
  20. Parsons K, McCormac A, Pattinson M, Butavicius M, Jerram C (2013) Phishing for the truth: a scenario-based experiment of users’ behavioural response to emails. In: Security and privacy protection in information processing systems, IFIP advances in information and communication technology, vol 405. Springer, Berlin, pp 366–378Google Scholar
  21. Pennacchiotti M, Popescu AM (2011) A machine learning approach to twitter user classification. In: Proceedings of the fifth international AAAI conference on weblogs and social mediaGoogle Scholar
  22. Rao D, Paul MJ, Fink C, Yarowsky D, Oates T, Coppersmith G (2011) Hierarchical Bayesian models for latent attribute detection in social media. In: Proceedings of the fifth international AAAI conference on weblogs and social medianGoogle Scholar
  23. Sculley D (2010) Web-scale k-means clustering. In: Proceedings of the 19th international conference on World Wide Web. ACM, New York, pp 1177–1178Google Scholar
  24. Staksrud E, Ólafsson K, Livingstone S (2013) Does the use of social networking sites increase children’s risk of harm? Comput Hum Behav 29(1):40–50CrossRefGoogle Scholar
  25. Vitak J (2012) The impact of context collapse and privacy on social network site disclosures. J Broadcast Electron Media 56(4):451–470Google Scholar
  26. Wondracek G, Holz T, Kirda E, Kruegel C (2010) A practical attack to de-anonymize social network users. In: 2010 IEEE symposium on security and privacy, Berkeley/Oakland, USA, pp 223–238Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Vilnius Gediminas Technical UniversityVilniusLithuania

Personalised recommendations