Advertisement

Evaluating the Propagation Strength of Malicious Metaphor in Social Network: Flow Through Inspiring Influence of Members

  • Manash Sarkar
  • Soumya Banerjee
  • Aboul Ella Hassanien
Part of the Intelligent Systems Reference Library book series (ISRL, volume 65)

Abstract

Interaction across social networking sites leads to different kinds of ideas, concepts and choices and sharing or some nourishing effects, which might influence others to believe or trust. Seldom may this cause some malicious effect for members and their peers only. As social network is the domain for sharing opinion and comments, subsequently it can also propagate malicious signature as well. Security and privacy is essential component to protect user profile from this kind of malicious program, which basically evolves from any close acquaintances, that also belongs to same vector plane. The degree of malicious attack of a social network depends on the number of flow links from one user to another with forward operations. It is true that the probability of malicious attack evolves from friend’s community is of greater attack prone magnitude than the degree of attack from unknown members. This paper focuses the different verticals of such possibilities of attack under social network processes and also tries to investigate the rudimentary precautionary measure pertaining security algorithm behind it.

Keywords

Degree of Attack Social Network Attack Graph Conditional Probability Gaussian distribution 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology 27(1), 415–444 (2001), http://www.annualreviews.org/doi/abs/10.1146/annurev.soc.27.1.415, doi:10.1146/annurev.soc.27.1.415
  2. 2.
    Golub, B., Jackson, M.O.: How homophily affects the speed of learning and best-response dynamics. The Quarterly Journal of Economics 127(3), 1287–1338 (2012)Google Scholar
  3. 3.
    Bisgin, H., Agarwal, N., Xu, X.: Investigating homophily in online social networks. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 533–536. IEEE (2010)Google Scholar
  4. 4.
    Ceglowski, M., Coburn, A., Cuadrado, J.: Semantic search of unstructured data using contextual network graphs. National Institute for Technology and Liberal Education 10 (2003)Google Scholar
  5. 5.
    Suchal, J.: On finding power method in spreading activation search. In: SOFSEM 2008, vol. II - Student Research Forum, pp. 124–130 (2007)Google Scholar
  6. 6.
    Jackson, M.O.: Average distance, diameter, and clustering in social networks with homophily. In: Papadimitriou, C., Zhang, S. (eds.) WINE 2008. LNCS, vol. 5385, pp. 4–11. Springer, Heidelberg (2008)Google Scholar
  7. 7.
    Sekine, K., Imai, H., Tani, S.: Computing the tutte polynomial of a graph of moderate size. In: Staples, J., Katoh, N., Eades, P., Moffat, A. (eds.) ISAAC 1995. LNCS, vol. 1004, pp. 224–233. Springer, Heidelberg (1995)Google Scholar
  8. 8.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth & Brooks, Monterey (1984)Google Scholar
  9. 9.
    Security, I., Committee, S.A., et al.: Ssac. sac 025-ssac, advisory on fast flux hosting and dns (2008)Google Scholar
  10. 10.
    Konte, M., Feamster, N., Jung, J.: Dynamics of online scam hosting infrastructure. In: Moon, S.B., Teixeira, R., Uhlig, S. (eds.) PAM 2009. LNCS, vol. 5448, pp. 219–228. Springer, Heidelberg (2009)Google Scholar
  11. 11.
    Veerarajan, T.: Probability, Statistics and Random Processes, 3rd edn. Tata McGraw-Hill Education (2008)Google Scholar
  12. 12.
    Bayer, U., Kruegel, C., Kirda, E.: Ttanalyze: A tool for analyzing malware. In: 15th European Institute for Computer Antivirus Research (EICAR 2006) Annual Conference (2006)Google Scholar
  13. 13.
    Choi, H., Lee, H., Lee, H., Kim, H.: Botnet detection by monitoring group activities in dns traffic. In: 7th IEEE International Conference on Computer and Information Technology, CIT 2007, pp. 715–720. IEEE (2007)Google Scholar
  14. 14.
    Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33(1), 1 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Manash Sarkar
    • 1
  • Soumya Banerjee
    • 1
  • Aboul Ella Hassanien
    • 2
  1. 1.Department of Computer ScienceBirla Institute of TechnologyMesraIndia
  2. 2.Faculty of Computers and InformationCairo UniversityCairoEgypt

Personalised recommendations