Skip to main content
SpringerLink
Log in

The Study of Content Security for Mobile Internet

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The vast amount of information carried over Mobile Internet and the high speed are providing unprecedented convenience for users, Mobile Internet is facing growing threat of lack of security. It is crucial to maintain and improve safety and security for Mobile Internet for it to thrive and develop. At content level, users are facing increasing amount of malicious or spam content, jeopardizing public’s interest in legitimate internet content. Therefore, Mobile Internet information security has become an important research topic. In this paper we first propose a framework for content security management system for Mobile Internet, and then discuss how to acquire relevant information from Mobile Internet in a fast and efficient manner, how to process and analyze the vast amount of information collected, how to quickly discover negative or illegal information within the network, and provide detection and early warnings for potential hot topics. At the same time, we study how to perform audit and evaluation on the information content so that the relevant security management actions can be done.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. http://www.cnnic.cn/dtygg/dtgg/201107/t20110719_22132.html.

  2. Ganter B., Wille R. (1999) Formal concept analysis-mathematical foundation. Springer, Berlin, New York

    Book  Google Scholar 

  3. Neuss C., Kent R. E. (1995) Conceptual analysis of resource meta-information. Computer Networks And ISDN Systems 27(6): 973–984

    Article  Google Scholar 

  4. Lindig, C., & Snelting, G. (1997). Assessing modular structure of Legacy code based on mathematical concept analysis. In International conference on software engineering (ICES97) (pp. 349–359). Boston, USA.

  5. Missaoui R., Godin R. (1994) Extracting exact and approximate rules from databases, incompleteness and uncertainty in information systems. Springer, London, pp 209–222

    Google Scholar 

  6. Baeza-Yates R., Ribeiro-Neto B. (1999) Modern information retrieval. ACM Press, New York

    Google Scholar 

  7. Shkapenyuk, V., & Suel, T. (2002). Design and implementation of a high-performance distributed web crawler. In Proceedings of the 18th international conference on data engineering (pp. 357–368). California, USA.

  8. Wan, Y., & Tong, H. (2008). URL assignment algorithm of crawler in distributed system based on hash. In IEEE international conference on networking, sensing and control, 2008. ICNSC 2008 (pp. 1632–1635).

  9. Guo J. (2009) Web search. Higher Education Press, China

    Google Scholar 

  10. http://www.lemurproject.org.

  11. Zelle, J. M. (1995). Using inductive logic programming to automate the construction of natural language parsers (pp. 96–249). Ph.D. thesis, Department of Computer Sciences, University of Texas at Austin.

  12. Zelle J. M., Mooney R. J. (1996) Learning to parse database queries using inductive logic programming. AAAI/IAAI 2: 1050–1055

    Google Scholar 

  13. Katz, B., Lin, J. J., & Felshin, S. (2002). The START multimedia information system: Current technology and future directions. In Proceedings of the International Workshop on Multimedia Information Systems (MIS) (pp. 117–123).

  14. Jones, C., Abdelmoty, A., Finch, D., Fu, G., & Vaid, S. (2004). The SPIRIT spatial search engine: Architecture, ontologies and spatial indexing. In Proceedings of the third international conference on geographic information science (pp. 125–139).

  15. Jones, C., Purves, R., Ruas, A., Sanderson, M., Sester, M., van Kreveld, M., et al. (2002). Spatial information retrieval and geographical ontologies—an overview of the SPIRIT project. In Proceedings of the 25th annual international conference on research and development in information retrieval.

  16. Li, Z. (2009). Research on geographic information retrieval. Ph.D. thesis, University of Science and Technology of China.

  17. Luo, W., Liu, Q., & Cheng, X. (2003). Development and analysis of technology of topic detection and tracking. In Proceedings of the JSCL 2003 (pp. 560–566). China, Beijing: Tsinghua University.

  18. Yu H., Yu Z., Liu T., Li S. (2007) Topic detection and tracking review. Journal of Chinese Information Processing 6(21): 77–79

    Google Scholar 

  19. Allan, J., Carbonell, J., Doddington, G., Yamron, J., & Yang, Y. (1998). Topic detection and tracking pilot study: Final report. In Proceedings of the DARPA broadcast news transcription and understanding workshop (pp. 194–218). Lansdowne, Virginia.

  20. Allan, J., Lavrenko, V., & Swan, R. (2002). Explorations within topic tracking and detection. In Topic detection and tracking: Event-based information organization(pp. 197–224). Massachusetts: Kluwer.

  21. http://www.freepatentsonline.com/4930077.html.

  22. http://sda.berkeley.edu/.

  23. http://www.nesstar.org/.

  24. Kumaran, & G., Allan, J. (2004). Text classification and named entities for new event detection. In Proceedings of the SIGIR conference on research and development in information retrieval (pp. 297–304). ACM: Sheffield, South Yorkshire.

  25. Allan, J., Jin, H., Rajman, M., Wayne, C., Hoberman, G., & Caputo, D. (1999). Topic-based novelty detection. In Proceedings of the Johns Hopkins summer workshop, CLSP, Baltimore.

  26. Yang, Y., Carbonell, J., & Jin, C. (2002). Topic-conditioned novelty detection. In Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 688–693). ACM Press: New York.

  27. Lam W., Meng H., Wong K., Yen J. (2001) Using contextual analysis for news event detection. International Journal on Intelligent Systems 16(4): 525–546

    Article  MATH  Google Scholar 

  28. Felzenszwalb P., Huttenlocher D. (2005) Representation and detection of deformable shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(2): 208–220

    Article  Google Scholar 

  29. Yang J., Frangi A., Yang J. (2005) KPCA plus LDA: A complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(2): 230–244

    Article  Google Scholar 

  30. Brendan J. F., Delbert D. (2007) Clustering by passing messages between data points. Science 315: 972–976

    Article  MathSciNet  MATH  Google Scholar 

  31. Seung H., Lee D. (2000) The manifold ways of perception. Science 290(12): 2268–2269

    Article  Google Scholar 

  32. Roweis S., Saul L. (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(12): 2323–2326

    Article  Google Scholar 

  33. Ye J., Li Q. (2005) A two-stage linear discriminant analysis via QR decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6): 929–941

    Article  Google Scholar 

  34. Guo, J., Guo, H., & Wang, Z. (2011). An activation force-based affinity measure for analyzing complex networks. Scientific report, 1, 113. http://www.nature.com/srep/2011/111012/srep00113/full/srep00113.html.

  35. Deng W., Guo J., Hu J., Zhang H. (2008) Comment on “100% accuracy in automatic face recognition”. Science 321(8): 912c

    Article  Google Scholar 

  36. Li F., Fergus R., Perona P. (2006) One-shot learning of object categories. IEEE Transactiopns on Pattern Analysis and Machine Intelligence 28(4): 594–611

    Article  Google Scholar 

  37. Carneiro G., Chan A., Moreno P., Vasconcelos N. (2007) Supervised learning of semantic classes for image annotation and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(3): 394–410

    Article  Google Scholar 

  38. Liu, Z., Zavesky, E., Gibbon, D., Shahraray, B., & Haffner, P. (2007). AT&T Research at TRECVID 2007. In Online proceedings of TRECVID.

  39. Turnbull, D., Barrington, L., Torres, D., & Lanckriet, G. (2007). Towards musical query by semantic description using the CAL500 data set. In SIGIR 2007 proceedings (pp. 439–446).

Download references

Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China

    Qianfang Xu, Jun Guo & Bo Xiao

Authors
  1. Qianfang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qianfang Xu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, Q., Guo, J. & Xiao, B. The Study of Content Security for Mobile Internet. Wireless Pers Commun 66, 523–539 (2012). https://doi.org/10.1007/s11277-012-0738-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-012-0738-8

Keywords

Search

Navigation