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A Research on the Identification of Internet User Based on Deep Learning

  • Hong Shao
  • Liujun Tang
  • Ligang Dong
  • Long Chen
  • Xian Jiang
  • Weiming Wang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)

Abstract

In the environment of big data, analyzing internet user behavior has become a research hot spot. By profiling the normal online behavior data of network users to learn their online habits and preferences, is not only helpful to provide network users with more efficient and personalized network services, but also to update the network security policies. Because there is no identification of network users in network management, network administrators need to develop and deliver relevant network services manually to user base on the network user Internet Protocol (IP) address. Therefore, this paper proposes the utilization of deep learning technology to identify network user automatically after fully understand the behavior of network user. At the first, a network identification model based on Deep Belief Network (DBN) is proposed. Then, we apply the Tensorflow framework to construct a DBN model suitable for network user identification. Finally, an experiment with real data set was undertaken upon the model to verify its accuracy on identifying network users. It is found that DBN-based identification model can achieve a high classification accuracy of user identity by constructing deep network structure.

Keywords

Deep learning Deep belief network User behavior profile 

References

  1. 1.
    Ma, J., Zhou, G., Xu, B., et al.: A microblog user impact analysis method based on the diffusion of topic. Univ. Inf. Eng. 14(6), 735–742 (2013)Google Scholar
  2. 2.
    Zhou, J.: Network User Behavior Analysis for SDN Firewall. Zhejiang Gongshang University (2017)Google Scholar
  3. 3.
    Ayeldeen, H., Hassanien, A.E., Fahmy, A.A.: Lexical similarity using fuzzy Euclidean distance. In: 2014 International Conference on Engineering and Technology (ICET), pp. 1–6. IEEE, (2014)Google Scholar
  4. 4.
    Celebi, M.E., Kingravi, H.A., Vela, P.A.A.: A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst. Appl. 40(1), 200–210 (2013)CrossRefGoogle Scholar
  5. 5.
    Guan, J., Yao, S., Xu, C., Zhang, H.: Design and implementation of network user behaviors analysis based on hadoop for big data. In: Batten, L., Li, G., Niu, W., Warren, M. (eds.) ATIS 2014. CCIS, vol. 490, pp. 44–55. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-45670-5_5CrossRefGoogle Scholar
  6. 6.
    Huang, S., Chen, K., Liu, C., et al.: A statistical-feature-based approach to internet traffic classification using machine learning. In: International Conference on Ultra Modern Telecommunications & Workshops, pp 1–6. IEEE (2009)Google Scholar
  7. 7.
    Le, Q.V.: Building high-level features using large scale unsupervised learning. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8595–8598. IEEE (2013)Google Scholar
  8. 8.
    Oravec, M., Podhradsky, P.: Medical image compression by backpropagation neural network and discrete orthogonal transforms. WIT Trans. Biomed. Heal. 4 (1970)Google Scholar
  9. 9.
    Paik, J.H.: A novel TF-IDF weighting scheme for effective ranking, pp. 343–352 (2013)Google Scholar
  10. 10.
    Ruijuan, Z., Jing, C., Mingchuan, Z., et al.: User abnormal behavior analysis based on neural network clustering. J. China Univ. Posts Telecommun. 23(3), 29–44 (2016)CrossRefGoogle Scholar
  11. 11.
    Tan, X., Xi, H.: Hidden semi-Markov model for anomaly detection. Appl. Math. Comput. 205(2), 562–567 (2008)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Hinton, G., Osindero, S.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Dong, F.: Studies and utilization on network users’ behavior analysis. Xidian University (2005)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Hong Shao
    • 1
  • Liujun Tang
    • 1
  • Ligang Dong
    • 1
  • Long Chen
    • 1
  • Xian Jiang
    • 1
  • Weiming Wang
    • 1
  1. 1.School of Information and Electronic EngineeringZhejiang Gongshang UniversityHangzhouChina

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