Advertisement

Restrain Malicious Attack Propagation

  • Jiaojiao Jiang
  • Sheng Wen
  • Shui Yu
  • Bo Liu
  • Yang Xiang
  • Wanlei Zhou
Chapter
Part of the Advances in Information Security book series (ADIS, volume 73)

Abstract

Restraining the propagation of malicious attacks in complex networks has long been an important but difficult problem to be addressed. In this chapter, we particularly use rumor propagation as an example to analyze the methods of restraining malicious attack propagation. There are mainly two types of methods: (1) blocking rumors at the most influential users or community bridges, and (2) spreading truths to clarify the rumors. We first compare all the measures of locating influential users. The results suggest that the degree and betweenness measures outperform all the others in real-world networks. Secondly, we analyze the method of the truth clarification method, and find that this method has a long-term performance while the degree measure performs well only in the early stage. Thirdly, in order to leverage these two methods, we further explore the strategy of different methods working together and their equivalence. Given a fixed budget in the real world, our analysis provides a potential solution to find out a better strategy by integrating both kinds of methods together.

References

  1. 5.
    R. Albert and A.-L. Barabási. Statistical mechanics of complex networks. Reviews of modern physics, 74(1):47, 2002.MathSciNetCrossRefGoogle Scholar
  2. 9.
    C. Anagnostopoulos, S. Hadjiefthymiades, and E. Zervas. Information dissemination between mobile nodes for collaborative context awareness. Mobile Computing, IEEE Transactions on, 10(12):1710–1725, 2011.CrossRefGoogle Scholar
  3. 25.
    C. Budak, D. Agrawal, and A. El Abbadi. Limiting the spread of misinformation in social networks. In Proceedings of the 20th international conference on World wide web, WWW ’11, pages 665–674. ACM, 2011.Google Scholar
  4. 26.
    S. Carmi, S. Havlin, S. Kirkpatrick, Y. Shavitt, and E. Shir. From the cover: A model of internet topology using k-shell decomposition. PNAS, Proceedings of the National Academy of Sciences, 104(27):11150–11154, 2007.CrossRefGoogle Scholar
  5. 28.
    CFinder. Clusters and communities, 2013.Google Scholar
  6. 29.
    D. Chakrabarti, J. Leskovec, C. Faloutsos, S. Madden, C. Guestrin, and M. Faloutsos. Information survival threshold in sensor and p2p networks. In INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE, pages 1316–1324, 2007.Google Scholar
  7. 31.
    Y. Chen, G. Paul, S. Havlin, F. Liljeros, and H. E. Stanley. Finding a better immunization strategy. Phys. Rev. Lett., 101:058701, Jul 2008.Google Scholar
  8. 33.
    A. Clauset, M. E. J. Newman, and C. Moore. Finding community structure in very large networks. Phys. Rev. E, 70:066111, Dec 2004.Google Scholar
  9. 34.
    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
  10. 41.
    Z. Dezső and A.-L. Barabási. Halting viruses in scale-free networks. Phys. Rev. E, 65:055103, May 2002.Google Scholar
  11. 49.
    H. Ebel, L.-I. Mielsch, and S. Bornholdt. Scale-free topology of e-mail networks. Phys. Rev. E, 66:035103, Sep 2002.Google Scholar
  12. 56.
    M. Faloutsos, P. Faloutsos, and C. Faloutsos. On power-law relationships of the internet topology. In Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication, SIGCOMM ’99, pages 251–262. ACM, 1999.Google Scholar
  13. 67.
    L. C. Freeman, S. P. Borgatti, and D. R. White. Centrality in valued graphs: a measure of betweenness based on network flow. Social Networks, 13:141–154, 1991.MathSciNetCrossRefGoogle Scholar
  14. 69.
    C. Gao and J. Liu. Modeling and restraining mobile virus propagation. Mobile Computing, IEEE Transactions on, 12(3):529–541, 2013.CrossRefGoogle Scholar
  15. 70.
    C. Gao, J. Liu, and N. Zhong. Network immunization and virus propagation in email networks: experimental evaluation and analysis. Knowledge and Information Systems, 27:253–279, 2011.CrossRefGoogle Scholar
  16. 71.
    C. Gao, J. Liu, and N. Zhong. Network immunization with distributed autonomy-oriented entities. Parallel and Distributed Systems, IEEE Transactions on, 22(7):1222–1229, 2011.CrossRefGoogle Scholar
  17. 74.
    N. Z. Gong, A. Talwalkar, L. Mackey, L. Huang, E. C. R. Shin, E. Stefanov, E. Shi, and D. Song. Joint link prediction and attribute inference using a social-attribute network. ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2013. Accepted.Google Scholar
  18. 80.
    P. Holme, B. J. Kim, C. N. Yoon, and S. K. Han. Attack vulnerability of complex networks. Phys. Rev. E, 65:056109, May 2002.Google Scholar
  19. 83.
    H. Jeong, S. P. Mason, A.-L. Barabási, and Z. N. Oltvai. Lethality and centrality in protein networks. Nature, 411(6833):41–42, 2001.CrossRefGoogle Scholar
  20. 96.
    M. Kitsak, L. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. Stanley, and H. Makse. Identification of influential spreaders in complex networks. Nature Physics, 6(11):888–893, Aug 2010.CrossRefGoogle Scholar
  21. 102.
    H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or a news media? In WWW ’10: Proceedings of the 19th international conference on World wide web, pages 591–600. ACM, 2010.Google Scholar
  22. 106.
    F. Li, Y. Yang, and J. Wu. Cpmc: An efficient proximity malware coping scheme in smartphone-based mobile networks. In INFOCOM, 2010 Proceedings IEEE, pages 1–9, 2010.Google Scholar
  23. 107.
    Y. Li, W. Chen, Y. Wang, and Z.-L. Zhang. Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships. In Proceedings of the sixth ACM international conference on Web search and data mining, WSDM ’13, pages 657–666. ACM, 2013.Google Scholar
  24. 109.
    Y. Li, B. Zhao, and J.-S. Lui. On modeling product advertisement in large-scale online social networks. Networking, IEEE/ACM Transactions on, 20(5):1412–1425, 2012.CrossRefGoogle Scholar
  25. 110.
    Y. Y. Liu, J. J. Slotine, and A. laszlo Barabasi. Controllability of complex networks. Nature, 473:167–173, 2011.CrossRefGoogle Scholar
  26. 120.
    H. E. Marano. Our brain’s negative bias. Technical report, Psychology Today, June 20, 2003.Google Scholar
  27. 122.
    R. M. May and A. L. Lloyd. Infection dynamics on scale-free networks. Phys. Rev. E, 64:066112, Nov 2001.Google Scholar
  28. 129.
    T. Nepusz and T. Vicsek. Controlling edge dynamics in complex networks. Nature, 8:568–573, 2012.Google Scholar
  29. 130.
    NetMiner4. Premier software for network analysis, 2013.Google Scholar
  30. 132.
    M. E. Newman. A measure of betweenness centrality based on random walks. Social networks, 27(1):39–54, 2005.CrossRefGoogle Scholar
  31. 136.
    M. E. J. Newman. Networks: An Introduction, chapter 17 Epidemics on networks, pages 700–750. Oxford University Press, 2010.Google Scholar
  32. 137.
    M. E. J. Newman and M. Girvan. Finding and evaluating community structure in networks. Phys. Rev. E, 69:026113, Feb 2004.Google Scholar
  33. 138.
    N. P. Nguyen, T. N. Dinh, S. Tokala, and M. T. Thai. Overlapping communities in dynamic networks: their detection and mobile applications. In Proceedings of the 17th annual international conference on Mobile computing and networking, MobiCom ’11, pages 85–96. ACM, 2011.Google Scholar
  34. 139.
    G. Palla, I. Derényi, I. Farkas, and T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043):814–818, 2005.CrossRefGoogle Scholar
  35. 140.
    G. Palla, I. Derényi, I. Farkas, and T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435:814–818, 2005.CrossRefGoogle Scholar
  36. 143.
    F. Peter. ‘bogus’ ap tweet about explosion at the white house wipes billions off us markets, April 23 2013. Washington.Google Scholar
  37. 147.
    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
  38. 159.
    M. A. Serrano and M. Boguñá. Clustering in complex networks. ii. percolation properties. Phys. Rev. E, 74:056115, Nov 2006.Google Scholar
  39. 165.
    S. Shirazipourazad, B. Bogard, H. Vachhani, A. Sen, and P. Horn. Influence propagation in adversarial setting: how to defeat competition with least amount of investment. In Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM ’12, pages 585–594. ACM, 2012.Google Scholar
  40. 171.
    B. Viswanath, A. Mislove, M. Cha, and K. P. Gummadi. On the evolution of user interaction in facebook. In Proceedings of the 2nd ACM workshop on Online social networks, WOSN ’09, pages 37–42, 2009.Google Scholar
  41. 173.
    M. Vojnovic, V. Gupta, T. Karagiannis, and C. Gkantsidis. Sampling strategies for epidemic-style information dissemination. Networking, IEEE/ACM Transactions on, 18(4):1013–1025, 2010.CrossRefGoogle Scholar
  42. 174.
    K. Wakita and T. Tsurumi. Finding community structure in mega-scale social networks: [extended abstract]. In Proceedings of the 16th international conference on World Wide Web, WWW ’07, pages 1275–1276, 2007.Google Scholar
  43. 184.
    S. Wen, J. Jiang, Y. Xiang, S. Yu, W. Zhou, and W. Jia. To shut them up or to clarify: restraining the spread of rumors in online social networks. Parallel and Distributed Systems, IEEE Transactions on, 25(12):3306–3316, 2014.CrossRefGoogle Scholar
  44. 185.
    S. Wen, W. Zhou, Y. Wang, W. Zhou, and Y. Xiang. Locating defense positions for thwarting the propagation of topological worms. Communications Letters, IEEE, 16(4):560–563, 2012.CrossRefGoogle Scholar
  45. 186.
    S. Wen, W. Zhou, J. Zhang, Y. Xiang, W. Zhou, and W. Jia. Modeling propagation dynamics of social network worms. Parallel and Distributed Systems, IEEE Transactions on, 24(8):1633–1643, 2013.CrossRefGoogle Scholar
  46. 190.
    Y. Xiang, X. Fan, and W. T. Zhu. Propagation of active worms: a survey. International journal of computer systems science & engineering, 24(3):157–172, 2009.Google Scholar
  47. 192.
    G. Yan, G. Chen, S. Eidenbenz, and N. Li. Malware propagation in online social networks: nature, dynamics, and defense implications. In Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security, ASIACCS’11, pages 196–206, 2011.Google Scholar
  48. 193.
    G. Yan and S. Eidenbenz. Modeling propagation dynamics of bluetooth worms (extended version). Mobile Computing, IEEE Transactions on, 8(3):353–368, 2009.CrossRefGoogle Scholar
  49. 195.
    K. Yang, A. H. Shekhar, D. Oliver, and S. Shekhar. Capacity-constrained network-voronoi diagram: a summary of results. In International Symposium on Spatial and Temporal Databases, pages 56–73. Springer, 2013.Google Scholar
  50. 206.
    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
  51. 207.
    C. C. Zou, D. Towsley, and W. Gong. Modeling and simulation study of the propagation and defense of internet e-mail worms. IEEE Transactions on dependable and secure computing, 4(2):105–118, 2007.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jiaojiao Jiang
    • 1
  • Sheng Wen
    • 1
  • Shui Yu
    • 3
  • Bo Liu
    • 2
  • Yang Xiang
    • 3
  • Wanlei Zhou
    • 4
  1. 1.Swinburne University of TechnologyHawthorne, MelbourneAustralia
  2. 2.La Trobe UniversityBundooraAustralia
  3. 3.University of Technology SydneyUltimoAustralia
  4. 4.Digital Research & Innovation CapabilitySwinburne University of TechnologyHawthorn, MelbourneAustralia

Personalised recommendations