On Complementary Effect of Blended Behavioral Analysis for Identity Theft Detection in Mobile Social Networks

  • Cheng Wang
  • Jing Luo
  • Bo Yang
  • Changjun Jiang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 747)


User behavioral analysis is expected to act as a promising technique for identity theft detection in the Internet. The performance of this paradigm extremely depends on a good individual-level user behavioral model. Such a good model for a specific behavior is often hard to obtain due to the insufficiency of data for this behavior. The insufficiency of specific data is mainly led by the prevalent sparsity of users’ collectable behavioral footprints. This work aims to address whether it is feasible to effectively detect identify thefts by jointly using multiple unreliable behavioral models from sparse individual-level records. We focus on this issue in mobile social networks (MSNs) with multiple dimensions of collectable but sparse data of user behavior, i.e., making check-ins, posing tips and forming friendships. Based on these sparse data, we build user spatial distribution model, user post interest model and user social preference model, respectively. Here, as the arguments, we validate that there is indeed a complementary effect in multi-dimensional blended behavioral analysis for identity theft detection in MSNs.


Mobile social networks Identity theft detection Blended behavioral analysis Complementary effect 



The research of authors is partially supported by the National Natural Science Foundation of China (NSFC) under Grants 61571331, Shuguang Program from Shanghai Education Development Foundation under Grant 14SG20, Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China under Grant 151066, and the Shanghai Science and Technology Innovation Action Plan Project under Grant 16511100901.


  1. 1.
    De Montjoye, Y.A., Radaelli, L., Singh, V.K., et al.: Unique in the shopping mall: On the reidentifiability of credit card metadata. Science 347(6221), 536–539 (2015)Google Scholar
  2. 2.
    Bao, J., Zheng, Y., Mokbel, M.F.: Location-based and preference-aware recommendation using sparse geo-social networking data. In: International Conference on Advances in Geographic Information Systems, pp. 199–208 (2012)Google Scholar
  3. 3.
    Lichman, M., Smyth, P.: Modeling human location data with mixtures of kernel densities. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 35–44 (2014)Google Scholar
  4. 4.
    Zhang, J., Chow, C.: CRATS: an lda-based model for jointly mining latent communities, regions, activities, topics, and sentiments from geosocial network data. IEEE Trans. Knowl. Data Eng. 28(11), 2895–2909 (2016)CrossRefGoogle Scholar
  5. 5.
    Zhou, X., Liang, X., Zhang, H., Ma, Y.: Cross-platform identification of anonymous identical users in multiple social media networks. IEEE Trans. Knowl. Data Eng. 28(2), 411–424 (2016)CrossRefGoogle Scholar
  6. 6.
    Daz-Santiago, S., Rodrguez-Henrquez, L.M., Chakraborty, D.: A cryptographic study of tokenization systems. Int. J. Inf. Secur. 15(4), 413–432 (2016)CrossRefGoogle Scholar
  7. 7.
    Naini, F.M., Unnikrishnan, J., Thiran, P., Vetterli, M.: Where you are is who you are: User identification by matching statistics. IEEE Trans. Inf. Forensics Secur. 11(2), 358–372 (2016)CrossRefGoogle Scholar
  8. 8.
    Wang, C., Zhou, J., Yang, B.: From footprint to friendship: modeling user followership in mobile social networks from check-in data. In: SIGIR 2017, pp. 825–828 (2017)Google Scholar
  9. 9.
    Kambourakis, G., Damopoulos, D., Papamartzivanos, D., Pavlidakis, E.: Introducing touchstroke: keystroke based authentication system for smartphones. Secur. Commun. Networks 9(6), 542–554 (2016)CrossRefGoogle Scholar
  10. 10.
    Zuo, Y., Wu, J., Zhang, H., Lin, H., Wang, F., Xu, K., Xiong, H.: Topic modeling of short texts: a pseudo-document view. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August, pp. 2105–2114 (2016)Google Scholar
  11. 11.
    He, Y., Wang, C., Jiang, C.: Mining coherent topics with pre-learned interest knowledge in Twitter. IEEE Access 5, 10515–10525 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Computer Science and TechnologyTongji UniversityShanghaiChina

Personalised recommendations