Skip to main content
SpringerLink
Log in

Comparative Study and Numerical Analysis

  • Chapter
  • First Online:
Malicious Attack Propagation and Source Identification

Part of the book series: Advances in Information Security ((ADIS,volume 73))

Abstract

This chapter provides an extensive literature review on identifying the propagation source of malicious attacks by tracing research trends and hierarchically reviewing the contributions along each research line regarding identifying the propagation source of malicious attacks. This chapter consists of three parts. We first review the existing approaches and analyze their pros and cons. Then, numerical studies are provided according to various experiment settings and diffusion scenarios. Finally, we summarize the remarks of existing approaches. Here, we particularly use rumor propagation as an example to analyze these approaches.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. A. Agaskar and Y. M. Lu. A fast monte carlo algorithm for source localization on graphs. In SPIE Optical Engineering and Applications. International Society for Optics and Photonics, 2013.

    Google Scholar 

  2. R. Albert, I. Albert, and G. L. Nakarado. Structural vulnerability of the north american power grid. Physical review E, 69(2):025103, 2004.

    Google Scholar 

  3. F. Altarelli, A. Braunstein, L. DallAsta, A. Lage-Castellanos, and R. Zecchina. Bayesian inference of epidemics on networks via belief propagation. Physical review letters, 112(11):118701, 2014.

    Google Scholar 

  4. E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts. Everyone’s an influencer: Quantifying influence on twitter. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM ’11, pages 65–74, 2011.

    Google Scholar 

  5. D. Brockmann and D. Helbing. The hidden geometry of complex, network-driven contagion phenomena. Science, 342(6164):1337–1342, 2013.

    Article  Google Scholar 

  6. C. H. Comin and L. da Fontoura Costa. Identifying the starting point of a spreading process in complex networks. Phys. Rev. E, 84:056105, Nov 2011.

    Google Scholar 

  7. D. Dagon, C. C. Zou, and W. Lee. Modeling botnet propagation using time zones. In NDSS, volume 6, pages 2–13, 2006.

    Google Scholar 

  8. W. Dong, W. Zhang, and C. W. Tan. Rooting out the rumor culprit from suspects. In Information Theory Proceedings (ISIT), 2013 IEEE International Symposium on, pages 2671–2675. IEEE, 2013.

    Google Scholar 

  9. V. Fioriti, M. Chinnici, and J. Palomo. Predicting the sources of an outbreak with a spectral technique. Applied Mathematical Sciences, 8(135):6775–6782, 2014.

    Article  Google Scholar 

  10. M. Fossi and J. Blackbird. Symantec internet security threat report 2010. Technical report, Symantec Corporation, March, 2011.

    Google Scholar 

  11. P. D. Grünwald. The minimum description length principle. MIT press, 2007.

    Google Scholar 

  12. S. L. Hakimi, M. L. Labbé, and E. Schmeichel. The voronoi partition of a network and its implications in location theory. ORSA journal on computing, 4(4):412–417, 1992.

    Article  MathSciNet  Google Scholar 

  13. S. Jitesh and A. Jafar. The enron email dataset database schema and brief statistical report. Technical report, University of Southern California, 2009.

    Google Scholar 

  14. N. Karamchandani and M. Franceschetti. Rumor source detection under probabilistic sampling. In Information Theory Proceedings (ISIT), 2013 IEEE International Symposium on, pages 2184–2188, 2013.

    Google Scholar 

  15. B. Karrer and M. E. J. Newman. Message passing approach for general epidemic models. Phys. Rev. E, 82:016101, Jul 2010.

    Google Scholar 

  16. A. Y. Lokhov, M. Mézard, H. Ohta, and L. Zdeborová. Inferring the origin of an epidemy with dynamic message-passing algorithm. arXiv preprint arXiv:1303.5315, 2013.

    Google Scholar 

  17. A. Louni and K. Subbalakshmi. A two-stage algorithm to estimate the source of information diffusion in social media networks. In Computer Communications Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on, pages 329–333. IEEE, 2014.

    Google Scholar 

  18. W. Luo and W. P. Tay. Finding an infection source under the sis model. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 2930–2934, 2013.

    Google Scholar 

  19. W. Luo, W. P. Tay, and M. Leng. Identifying infection sources and regions in large networks. Signal Processing, IEEE Transactions on, 61(11):2850–2865, 2013.

    Article  MathSciNet  Google Scholar 

  20. W. Luo, W. P. Tay, and M. Leng. How to identify an infection source with limited observations. IEEE Journal of Selected Topics in Signal Processing, 8(4):586–597, 2014.

    Article  Google Scholar 

  21. D. Moore, V. Paxson, S. Savage, C. Shannon, S. Staniford, and N. Weaver. Inside the slammer worm. IEEE Security and Privacy, 1(4):33–39, July 2003.

    Article  Google Scholar 

  22. P. C. Pinto, P. Thiran, and M. Vetterli. Locating the source of diffusion in large-scale networks. Phys. Rev. Lett., 109, Aug 2012.

    Google Scholar 

  23. B. A. Prakash, J. Vreeken, and C. Faloutsos. Spotting culprits in epidemics: How many and which ones? In Proceedings of the 2012 IEEE 12th International Conference on Data Mining, ICDM ’12, pages 11–20, Washington, DC, USA, 2012. IEEE Computer Society.

    Google Scholar 

  24. B. A. Prakash, J. Vreeken, and C. Faloutsos. Efficiently spotting the starting points of an epidemic in a large graph. Knowledge and Information Systems, 38(1):35–59, 2014.

    Article  Google Scholar 

  25. J. G. Restrepo, E. Ott, and B. R. Hunt. Characterizing the dynamical importance of network nodes and links. Phys. Rev. Lett., 97:094102, Sep 2006.

    Google Scholar 

  26. V. Sekar, Y. Xie, D. A. Maltz, M. K. Reiter, and H. Zhang. Toward a framework for internet forensic analysis. In ACM HotNets-III, 2004.

    Google Scholar 

  27. E. Seo, P. Mohapatra, and T. Abdelzaher. Identifying rumors and their sources in social networks. In SPIE Defense, Security, and Sensing, volume 8389, 2012.

    Google Scholar 

  28. D. Shah and T. Zaman. Detecting sources of computer viruses in networks: Theory and experiment. In Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS ’10, pages 203–214. ACM, 2010.

    Google Scholar 

  29. D. Shah and T. Zaman. Rumors in a network: Who’s the culprit? IEEE Transactions on information theory, 57(8):5163–5181, 2011.

    Article  MathSciNet  Google Scholar 

  30. D. Shah and T. Zaman. Rumor centrality: A universal source detector. SIGMETRICS Perform. Eval. Rev., 40(1):199–210, June 2012.

    Article  Google Scholar 

  31. Y. Wang, S. Wen, Y. Xiang, and W. Zhou. Modeling the propagation of worms in networks: A survey. Communications Surveys Tutorials, IEEE, PP(99):1–19, 2013.

    Google Scholar 

  32. Z. Wang, W. Dong, W. Zhang, and C. W. Tan. Rumor source detection with multiple observations: Fundamental limits and algorithms. In The 2014 ACM International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS ’14, pages 1–13. ACM, 2014.

    Google Scholar 

  33. D. J. Watts and S. H. Strogatz. Collective dynamics of ‘small-world’ networks. nature, 393(6684):440–442, 1998.

    Article  Google Scholar 

  34. N. Weaver, V. Paxson, S. Staniford, and R. Cunningham. A taxonomy of computer worms. In Proceedings of the 2003 ACM Workshop on Rapid Malcode, WORM ’03, pages 11–18, 2003.

    Google Scholar 

  35. P. Wood and G. Egan. Symantec internet security threat report 2011. Technical report, Symantec Corporation, April, 2012.

    Google Scholar 

  36. Y. Xie, V. Sekar, D. A. Maltz, M. K. Reiter, and H. Zhang. Worm origin identification using random moonwalks. In Security and Privacy, 2005 IEEE Symposium on, pages 242–256. IEEE, 2005.

    Google Scholar 

  37. W. Zang, P. Zhang, C. Zhou, and L. Guo. Discovering multiple diffusion source nodes in social networks. Procedia Computer Science, 29:443–452, 2014.

    Article  Google Scholar 

  38. G.-M. Zhu, H. Yang, R. Yang, J. Ren, B. Li, and Y.-C. Lai. Uncovering evolutionary ages of nodes in complex networks. The European Physical Journal B, 85(3):1–6, 2012.

    Article  Google Scholar 

  39. K. Zhu and L. Ying. Information source detection in the sir model: A sample path based approach. In Information Theory and Applications Workshop (ITA), pages 1–9, 2013.

    Google Scholar 

  40. K. Zhu and L. Ying. A robust information source estimator with sparse observations. Computational Social Networks, 1(1):1, 2014.

    Google Scholar 

  41. Y. Zhu, B. Xu, X. Shi, and Y. Wang. A survey of social-based routing in delay tolerant networks: Positive and negative social effects. Communications Surveys Tutorials, IEEE, 15(1):387–401, Jan 2013.

    Article  Google Scholar 

  42. Z. Zhu, G. Lu, Y. Chen, Z. Fu, P. Roberts, and K. Han. Botnet research survey. In Computer Software and Applications, 2008. COMPSAC ’08. 32nd Annual IEEE International, pages 967–972, July 2008.

    Google Scholar 

  43. C. C. Zou, W. Gong, and D. Towsley. Code red worm propagation modeling and analysis. In Proceedings of the 9th ACM Conference on Computer and Communications Security, CCS ’02, pages 138–147, 2002.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Swinburne University of Technology, Hawthorne, Melbourne, VIC, Australia

    Jiaojiao Jiang & Sheng Wen

  2. La Trobe University, Bundoora, VIC, Australia

    Bo Liu

  3. University of Technology Sydney, Ultimo, NSW, Australia

    Shui Yu & Yang Xiang

  4. Digital Research & Innovation Capability, Swinburne University of Technology, Hawthorn, Melbourne, VIC, Australia

    Wanlei Zhou

Authors
  1. Jiaojiao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sheng Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shui Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yang Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wanlei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jiang, J., Wen, S., Yu, S., Liu, B., Xiang, Y., Zhou, W. (2019). Comparative Study and Numerical Analysis. In: Malicious Attack Propagation and Source Identification. Advances in Information Security, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-030-02179-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-02179-5_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02178-8

  • Online ISBN: 978-3-030-02179-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation