MetaCom: Profiling Meta Data to Detect Compromised Accounts in Online Social Networks

  • Ravneet KaurEmail author
  • Sarbjeet Singh
  • Harish Kumar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1113)


Social networks have become the center of research and its increasing popularity has also led to its misuse by a number of malicious users. In order to conduct various malicious activities on the online platforms, malevolent users rely on spam, fake or compromised accounts to disseminate their illegitimate information. This paper addresses the detection of compromised accounts so that the concerned user can take the necessary action to mitigate the effect of compromise. Unlike most of the existing techniques where text based features are used to address the problem, this research examines the efficiency of meta data information associated with each text in detecting the compromised accounts. Secondly, we have studied the problem from both unary as well as binary classification perspectives where efficiency of respective machine learning classifiers have been analyzed on the basis of different evaluation metrics. Amongst five binary classifiers, Random Forest attained highest efficiency achieving 92.66% F-score. On the other hand, with one class classifiers, OCC-SVM with rbf kernel attained maximum performance with an average F-score of 72.72%.


Compromised accounts Binary classification Unary classification Online social networks 



This publication is an outcome of the R&D work under Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation under Grant Number: PhD/MLA/4(61)/2015-16.


  1. 1.
    Adan, A., Archer, S.N., Hidalgo, M.P., Di Milia, L., Natale, V., Randler, C.: Circadian typology: a comprehensive review. Chronobiol. Int. 29(9), 1153–1175 (2012)CrossRefGoogle Scholar
  2. 2.
    Adewole, K.S., Anuar, N.B., Kamsin, A., Varathan, K.D., Razak, S.A.: Malicious accounts: dark of the social networks. J. Netw. Comput. Appl. 79, 41–67 (2017)CrossRefGoogle Scholar
  3. 3.
    Al-Ayyoub, M., Jararweh, Y., Rabab’ah, A., Aldwairi, M.: Feature extraction and selection for arabic tweets authorship authentication. J. Ambient Intell. Humaniz. Comput. 8(3), 383–393 (2017)CrossRefGoogle Scholar
  4. 4.
    Barbon, S., Igawa, R.A., Zarpelão, B.B.: Authorship verification applied to detection of compromised accounts on online social networks. Multimed. Tools Appl. 76(3), 3213–3233 (2017)CrossRefGoogle Scholar
  5. 5.
    Brocardo, M.L., Traore, I., Woungang, I.: Authorship verification of e-mail and tweet messages applied for continuous authentication. J. Comput. Syst. Sci. 81(8), 1429–1440 (2015)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Brocardo, M.L., Traore, I., Woungang, I.: Continuous authentication using writing style. In: Obaidat, M.S., Traore, I., Woungang, I. (eds.) Biometric-Based Physical and Cybersecurity Systems, pp. 211–232. Springer, Cham (2019). Scholar
  7. 7.
    Brocardo, M.L., Traore, I., Woungang, I., Obaidat, M.S.: Authorship verification using deep belief network systems. Int. J. Commun. Syst. 30(12), e3259 (2017)CrossRefGoogle Scholar
  8. 8.
    Egele, M., Stringhini, G., Kruegel, C., Vigna, G.: COMPA: detecting compromised accounts on social networks. In: NDSS (2013)Google Scholar
  9. 9.
    Egele, M., Stringhini, G., Kruegel, C., Vigna, G.: Towards detecting compromised accounts on social networks. IEEE Trans. Dependable Secure Comput. 14(4), 447–460 (2017)CrossRefGoogle Scholar
  10. 10.
    Farseev, A., Nie, L., Akbari, M., Chua, T.S.: Harvesting multiple sources for user profile learning: a big data study. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 235–242. ACM (2015)Google Scholar
  11. 11.
    Fire, M., Kagan, D., Elyashar, A., Elovici, Y.: Friend or Foe? Fake profile identification in online social networks. Soc. Netw. Anal. Min. 4(1), 194 (2014)CrossRefGoogle Scholar
  12. 12.
    Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., Zhao, B.Y.: Detecting and characterizing social spam campaigns. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp. 35–47. ACM (2010)Google Scholar
  13. 13.
    Hu, X., Li, B., Zhang, Y., Zhou, C., Ma, H.: Detecting compromised email accounts from the perspective of graph topology. In: Proceedings of the 11th International Conference on Future Internet Technologies, pp. 76–82. ACM (2016)Google Scholar
  14. 14.
    Igawa, R.A., Almeida, A., Zarpelão, B., Barbon Jr., S.: Recognition on online social network by user’s writing style. iSys-Revista Brasileira de Sistemas de Informação 8(3), 64–85 (2016)Google Scholar
  15. 15.
    Jankowska, M., Keselj, V., Milios, E.: Proximity based one-class classification with common n-gram dissimilarity for authorship verification task. In: CLEF 2013 Evaluation Labs and Workshop-Working Notes Papers, pp. 23–26 (2013)Google Scholar
  16. 16.
    Johansson, F., Kaati, L., Shrestha, A.: Time profiles for identifying users in online environments. In: 2014 IEEE Joint Intelligence and Security Informatics Conference, pp. 83–90. IEEE (2014)Google Scholar
  17. 17.
    Kaur, R., Singh, S., Kumar, H.: AuthCom: authorship verification and compromised account detection in online social networks using ahp-topsis embedded profiling based technique. Expert Syst. Appl. 113, 397–414 (2018)CrossRefGoogle Scholar
  18. 18.
    Koppel, M., Schler, J.: Authorship verification as a one-class classification problem. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 62. ACM (2004)Google Scholar
  19. 19.
    Laleh, N., Carminati, B., Ferrari, E.: Anomalous change detection in time-evolving OSNs. In: 2016 Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), pp. 1–8. IEEE (2016)Google Scholar
  20. 20.
    Lee, S., Kim, J.: Warningbird: a near real-time detection system for suspicious urls in twitter stream. IEEE Trans. Dependable Secure Comput. 10(3), 183–195 (2013)CrossRefGoogle Scholar
  21. 21.
    Li, J.S., Chen, L.C., Monaco, J.V., Singh, P., Tappert, C.C.: A comparison of classifiers and features for authorship authentication of social networking messages. Concurr. Comput. Pract. Exp. 29(14), 1–15 (2016)Google Scholar
  22. 22.
    Nauta, M.: Detecting hacked twitter accounts by examining behavioural change using twitter metadata (2016)Google Scholar
  23. 23.
    Nauta, M., Habib, M., van Keulen, M.: Detecting hacked twitter accounts based on behavioural change. In: Proceedings of the 13th International Conference on Web Information Systems and Technologies, pp. 19–31 (2017)Google Scholar
  24. 24.
    Neal, T., Sundararajan, K., Woodard, D.: Exploiting linguistic style as a cognitive biometric for continuous verification. In: 2018 International Conference on Biometrics (ICB), pp. 270–276. IEEE (2018)Google Scholar
  25. 25.
    Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  26. 26.
    Peng, J., Choo, K.K.R., Ashman, H.: Bit-level n-gram based forensic authorship analysis on social media: Identifying individuals from linguistic profiles. J. Netw. Comput. Appl. 70, 171–182 (2016)CrossRefGoogle Scholar
  27. 27.
    Ruan, X., Wu, Z., Wang, H., Jajodia, S.: Profiling online social behaviors for compromised account detection. IEEE Trans. Inf. Forensics Secur. 11(1), 176–187 (2015)CrossRefGoogle Scholar
  28. 28.
    Sahoo, S.R., Gupta, B.B.: Classification of various attacks and their defence mechanism in online social networks: a survey. Enterp. Inf. Syst. 13(6), 832–864 (2019)CrossRefGoogle Scholar
  29. 29.
    Trång, D., Johansson, F., Rosell, M.: Evaluating algorithms for detection of compromised social media user accounts. In: 2015 Second European Network Intelligence Conference (ENIC), pp. 75–82. IEEE (2015)Google Scholar
  30. 30.
    VanDam, C., Tang, J., Tan, P.N.: Understanding compromised accounts on twitter. In: Proceedings of the International Conference on Web Intelligence, pp. 737–744. ACM (2017)Google Scholar
  31. 31.
    Viswanath, B., et al.: Towards detecting anomalous user behavior in online social networks. In: 23rd USENIX Security Symposium (USENIX Security 2014), pp. 223–238 (2014)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University Institute of Engineering and Technology, Panjab UniversityChandigarhIndia

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