Abstract
Network security is not only related to social stability, but also an important guarantee for the digital intelligent society. However, in recent years, problems such as user account theft and information leakage have occurred frequently, which has greatly affected the security of users’ personal information and the public interest. Based on the massive user click behavior data and graph embedding technology, this paper proposes the Graph2Usersim (Gp2-US) to analyze the similarity between the user’s historical behavior characteristics and online behavior characteristics to accurately identify the user’s identity, and then distinguish the user’s abnormal online behavior. To be specific, firstly, based on the empirical click-stream data, the user’s historical behavior and online behavior are modeled as two attention flow networks respectively. Secondly, based on the Graph2vec method and drawing on the theory of molecular fingerprinting, the nodes of the attention flow network are characterized as atoms, and edges are characterized as chemical bonds, and the network is simplified using the structural features of compounds to generate feature vectors that can identify users’ historical and online behaviors. Finally, the behavior similarity algorithm Gp2-US proposed in this paper is used to accurately identify users. A large number of experiments show that the accuracy of the algorithm Gp2-US is much higher than that of the traditional algorithm. Based on 10 days of historical user behavior data, it can accurately identify its identity characteristics and accurately determine abnormal account behavior. The research conclusions of this paper have important theoretical value and practical significance in inferring abnormal user behaviors and monitoring public opinion.
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References
Guo, J.Y., Li, R.H., Zhang, Y., Wang, G.R.: Graph neural network based anomaly detection in dynamic networks. J. Softw. 31(3), 748–762 (2020)
Echihabi, K.: High-dimensional vector similarity search: from time series to deep network embeddings. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 2829–2832 (2020)
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., Riley, P.: Molecular graph convolutions: moving beyond fingerprints. J. Comput. Aided Mol. Des. 30(8), 595–608 (2016)
Weng, L., Flammini, A., Vespignani, A., Menczer, F.: Competition among memes in a world with limited attention. Sci. Rep. 2(1), 335 (2012)
Li, Y., Zhang, J., Meng, X.-F., Wang, C.-Q.: Quantifying the influence of websites based on online collective attention flow. J. Comput. Sci. Technol. 30(6), 1175–1187 (2015)
Shi, P., Huang, X., Wang, J., Jiang, Z., Wu, Y.: A geometric representation of collective attention flows. PLOS ONE 10(9), e0136243 (2015)
Li, Y., Meng, X.F., Zhang, Q., Zhang, J., Wang, C.Q.: Common patterns of online collective attention flow. Sci. China Inf. Sci. 60(5), 059102 (2017)
Tang, X., Miao, Q.G., Quan, Y.N., Tang, J., Deng, K.: Predicting individual retweet behavior by user similarity: a multi-task learning approach. Knowl.-Based Syst. 8, 681–688 (2015)
Esuli, A., Petry, L.M., Renso, C., Bogomy, V.: Traj2User: exploiting embeddings for computing similarity of users mobile behavior (2018). https://arxiv.org/abs/1808.00554. Accessed 10 Oct 2021
Zhong, H.D., Lyu, H.B., Zhang, S.D., Ping, L., Justin, Z., Li, X.: Measuring user similarity using check-ins from LBSN: a mobile recommendation approach for e-commerce and security services. Enterp. Inf. Syst. 14(1), 1–20 (2019). https://doi.org/10.1080/17517575.2019.1686655
Bhuvaneswari, M.S., Muneeswaran, K.: User community detection from web server log using between user similarity metric. Int. J. Comput. Intell. Syst. 14(1), 266 (2020)
Qian, X.D., Li, M.: E-commerce user type recognition based on access sequence similarity. J. Organ. Comput. Electron. Commer. 30(3), 209–223 (2020)
Jia, Z.S., Han, Z.: Research and analysis of user behavior fingerprint on security situational awareness based on DNS Log. In: 6th International Conference on Behavioral, Economic and Socio-Cultural Computing, pp. 1–4 (2019). https://doi.org/10.1109/BESC48373.2019.8963120
Duvenaud, D., et al.: Convolutional networks on graphs for learning molecular fingerprints. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, pp. 2224–2232 (2015)
Wu, Z., et al.: MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9(2), 513–530 (2018)
Cereto-Massagué, A., Montes, M., Valls, C., Mulero, M., Garcia-Vallve, S., Pujadas, G.: Molecular fingerprint similarity search in virtual screening. Methods 71, 58–63 (2015)
Cai, H., Zheng, V.W., Chang, C.C.: A comprehensive survey of graph embedding: problems, techniques and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616–1637 (2017)
Satish, N., et al.: Navigating the maze of graph analytics frameworks using massive graph datasets. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 979–990 (2014)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Wang, D.X., Cui, P., Zhu, W.W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234 (2016)
Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey. Knowl.-Based Syst. 151, 78–94 (2017)
Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L.H, Liu, Y., Jaiswal, S.: graph2vec: Learning Distributed Representations of Graphs (2017). https://arxiv.org/abs/1707.05005. Accessed 10 Oct 2021
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning, pp. 1188–1196 (2014)
Grohe, M.: word2vec, node2vec, graph2vec, X2vec: towards a theory of vector embeddings of structured data. In: Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pp. 1–20 (2020)
Acknowledgements
This research was partially supported by the grants from the Natural Science Foundation of China (No. 71764025, 61863032); major scientific research projects of Northwest Normal University (NWNU-LKZD2021-06); the Research Project on Association of Fundamental Computing Education in Chinese Universities (Grant No. 2020-AFCEC-355); the Research Project on Educational Science Planning of Gansu, China (Grant No. GS [2018] GHBBKZ021). Author contributions: Yong Li and Zhongying Zhang are co-first authors who jointly designed the research.
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Li, Y., Zhang, Z., Wu, J., Zhang, Q. (2022). Research on User Identity Authentication Based on Online Behavior Similarity. In: Liao, X., et al. Big Data. BigData 2022. Communications in Computer and Information Science, vol 1496. Springer, Singapore. https://doi.org/10.1007/978-981-16-9709-8_18
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DOI: https://doi.org/10.1007/978-981-16-9709-8_18
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