Abstract
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.
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One of the largest statistics portals, http://www.statista.com/.
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The largest privately held cybersecurity organization based in the USA, operating globally across North America, EMEA and APAC, https://www.webroot.com/.
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Acknowledgments
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.
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Wang, C., Luo, J., Yang, B., Jiang, C. (2018). On Complementary Effect of Blended Behavioral Analysis for Identity Theft Detection in Mobile Social Networks. In: Zhu, L., Zhong, S. (eds) Mobile Ad-hoc and Sensor Networks. MSN 2017. Communications in Computer and Information Science, vol 747. Springer, Singapore. https://doi.org/10.1007/978-981-10-8890-2_3
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DOI: https://doi.org/10.1007/978-981-10-8890-2_3
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