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Applying Percolation Theory

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Cyber Resilience of Systems and Networks

Part of the book series: Risk, Systems and Decisions ((RSD))

Abstract

Unlike the previous chapter where propagation of failures along the dependency links was studied in a qualitative, human-judgment fashion, this chapter offers an approach to analyzing resilience to failure propagation via a rigorous use of percolation theory. In percolation theory, the basic idea is that a node failure or an edge failure (reverse) percolates throughout a network, and, accordingly, the failure affects the connectivity among nodes. The degree of network resilience can be measured by the size of a largest component (or cluster) after a fraction of nodes or edges are removed in the network. In many cybersecurity applications, the underlying ideas of percolation theory have not been much explored. In this chapter, it is explained how percolation theory can be used to measure network resilience in the process of dealing with different types of network failures. It introduces the measurement of adaptability and recoverability in addition to that of fault tolerance as new contributions to measuring network resilience by applying percolation theory.

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References

  • Albert, R., Jeong, H., & Barabási, A. L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382.

    Article  Google Scholar 

  • Avizienis, A., Laprie, J.-C., Randell, B., & Landwehr, C. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE Transactions on Dependable and Secure Computing, 1(1), 11–33.

    Article  Google Scholar 

  • Bagrow, J. P., Lehmann, S., & Ahn, Y.-Y. (2015). Robustness and modular structure in networks. Network Science, 3(4), 509–525.

    Article  Google Scholar 

  • Barabási, A.-L. (2016). Network science. Cambridge University Press, Cambridge, UK.

    Google Scholar 

  • Blume, L., Easley, D., Kleinberg, J., Kleinberg, R., & Tardos, É. (2011). Which Networks are Least Susceptible to Cascading Failures? In IEEE 52nd Annual Symposium on Foundations of Computer Science, pp. 393–402, Palm Springs.

    Google Scholar 

  • Broadbent, S., & Hammersley, J. (1957). Percolation processes I. Crystals and mazes. Mathematical Proceedings of the Cambridge Philosophical Society, 53(3), 629–641.

    Article  MathSciNet  Google Scholar 

  • Budak, C., Agrawal, D., & Abbadi, A. E. (2011). Limiting the spread of misinformation in social networks. ACM International World Wide Web Conference.

    Google Scholar 

  • Callaway, D. S., Newman, M. E. J., Strogatz, S. H., & Watts, D. J. (2000). Network robustness and fragility: Percolation on random graphs. Physical Review Letters, 85(25), 5468–5471.

    Article  Google Scholar 

  • Chau, C.-K., Gibbens, R. J., Hancock, R. E., & Towsley, D. (2011). Robust multipath routing in large wireless networks. Shanghai: Proc. of the IEEE INFOCOM.

    Book  Google Scholar 

  • Chen, P.-Y., Cheng, S.-M., & Chen, K.-C. (2012). Smart attacks in smart grid communication networks. IEEE Communications Magazine, 50(8), 24–29.

    Article  Google Scholar 

  • Cho, J. H., & Gao, J. (2016). Cyber war game in temporal networks. PLoS One, 11(2), e0148674.

    Article  Google Scholar 

  • Cho, J. H., Hurley, P., & Xu, H. (2016). Metrics and measurement of trustworthy systems. Baltimore: IEEE Military Communication Conference (MILCOM).

    Book  Google Scholar 

  • Cho, J. H., Xu, S., Hurley, P., Mackay, M., & Benjamin, T. (2017). STRAM: Measuring the trustworthiness of computer-based systems, ACM Computing Surveys (under review).

    Google Scholar 

  • Chung, F. (2014). A brief survey of PageRank algorithms. IEEE Transactions on Network Science and Engineering, 1(1), 38–42.

    Article  MathSciNet  Google Scholar 

  • Cohen, R., Erez, K., Ben-Avraham, D., & Havlin, S. (2000). Resilience of the internet to random breakdowns. Physical Review Letters, 85(21), 4626–4628.

    Article  Google Scholar 

  • Colbourn, C. (1987). Network resilience. SIAM Journal on Algebraic Discrete Methods, 8(3), 404–409.

    Article  MathSciNet  Google Scholar 

  • Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world, chapter 19: Cascading behavior in networks. Cambridge University Press, Cambridge, UK.

    Google Scholar 

  • Erdös, P., & Rényi, A. (1960). On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5, 17–61.

    MathSciNet  MATH  Google Scholar 

  • Farr, R. S., Harer, J. L., & Fink, T. M. (2014). Easily repairable networks: Reconnecting nodes after damage. Physical Review Letters, 113(13), 138701.

    Article  Google Scholar 

  • Freixas, J., & Pons, M. (2008). The influence of the node criticality relation on some measures of component importance. Operations Research Letters, 36(5), 557–560.

    Article  MathSciNet  Google Scholar 

  • Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826.

    Article  MathSciNet  Google Scholar 

  • Goel, S., Aggarwal, V., Yener, A., & Calderbank, A. R. (2011). The effect of eavesdroppers on network connectivity: A secrecy graph approach. IEEE Transactions on Information Forensics and Security, 6(3), 712–724.

    Article  Google Scholar 

  • Haimes, Y. Y. (2009). On the definition of resilience in systems. Risk Analysis, 29(4), 498–501.

    Article  MathSciNet  Google Scholar 

  • Huang, Z., Wang, C., Nayak, A., & Stojmenovic, I. (2015). Small cluster in cyber physical systems: Network topology, interdependence and cascading failures. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2340–2351.

    Article  Google Scholar 

  • Kong, Z., & Yeh, E. M. (2009). Wireless network resilience to degree-dependent and cascading node failures. In 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, pp. 1–6, Seoul.

    Google Scholar 

  • Linkov, I., Eisenberg, D. A., Plourde, K., Seager, T. P., Allen, J., & Kott, A. (2013). Resilience metrics for cyber systems. Environment Systems and Decisions, 33(4), 471–476.

    Article  Google Scholar 

  • Liu, G., Zhang, J., & Chen, G. (2014). An approach to finding the cost-effective immunization targets for information assurance. Decision Support Systems, 67, 40–52.

    Article  Google Scholar 

  • Majdandzic, A., Podobnik, B., Buldrev, S. V., Kenett, D. Y., Havlin, S., & Stanley, H. E. (2014). Spontaneous recovery in dynamical networks. Nature Physics, 10, 34–38.

    Article  Google Scholar 

  • McAuley, J., & Leskovec, J. (2012). Learning to discover social circles in ego networks. NIPS, 272, 548–556.

    Google Scholar 

  • Mizutaka, S., & Yakubo, K. (2013). Overload network failures: an approach from the random-walk model. In 2013 International Conference on Signal-Image Technology & Internet-Based Systems, pp. 630–633, Kyoto.

    Google Scholar 

  • Moore, C., & Newman, M. (2000). Epidemics and percolation in small-world networks. Physical Review E, 61(5), 5678–5682.

    Article  Google Scholar 

  • Najjar, W., & Gaudiot, J.-L. (1990). Network resilience: A measure of network fault tolerance. IEEE Transactions on Computers, 39(2), 174–181.

    Article  Google Scholar 

  • Newman, M. E. J. (2010a). Networks: An introduction, chapter 16: Percolation and network resilience (1st ed.). Oxford University Press, Oxford, UK.

    Chapter  Google Scholar 

  • Newman, M. E. J. (2010b). Networks: An introduction, chapter 17: Epidemics on networks (1st ed.). Oxford University Press, Oxford, UK.

    Google Scholar 

  • Newman, M. E. J. (2010c). Networks: An introduction, chapter 6: Measures and metrics (1st ed.). Oxford University Press, Oxford, UK.

    Google Scholar 

  • Newman, M., & Watts, D. (1999). Scaling and percolation in the small-world network model. Physical Review E, 60(6), 7332–7342.

    Article  Google Scholar 

  • Newman, M., & Ziff, R. (2001). Fast Monte Carlo algorithm for site or bond percolation. Physical Review E, 64(1), 016706.

    Article  Google Scholar 

  • Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435, 814–818.

    Article  Google Scholar 

  • Premm Raj, H., & Narahari, Y. (2012, August). Influence Limitation in Multi-Campaign Social Networks: A Shapley Value Based Approach. In 8th IEEE International Conference on Autonomous Science and Engineering, pp. 735–740, Seoul, Korea.

    Google Scholar 

  • Shao, S., Huang, X., Stanley, H. E., & Havlin, S. (2015). Percolation of localized attack on complex networks. New Journal of Physics, 17(2), 023049.

    Article  MathSciNet  Google Scholar 

  • Shekhtman, L., Danziger, M. M., & Havlin, S. (2016). Recent advances on failure and recovery in networks. Chaos, Solitons, and Fractals, 90, 28–36.

    Article  Google Scholar 

  • Sterbenz, J. P. G., Hutchison, D., Çetinkaya, E. K., Jabbar, A., Rohrer, J. P., Schöller, M., & Smith, P. (2010). Resilience and survivability in communication networks: Strategies, principles, and survey of disciplines. Computer Networks, 54(8), 1245–1265.

    Article  Google Scholar 

  • Sun, L., & Wang, W. (2013). Understanding blackholes in large-scale cognitive radio networks under generic failures (pp. 728–736). Turin: 2013 Proc. IEEE INFOCOM.

    Google Scholar 

  • Xing, F., & Wang, W. (2008). On the critical phase transition time of wireless multi-hop networks with random failure. Proc. of ACM MobiCom, San Francisco.

    Google Scholar 

  • Xu, Y., & Wang, W. (2010). Characterizing the spread of correlated failures in large wireless networks (pp. 1–9). San Diego: 2010 Proc. IEEE INFOCOM.

    Google Scholar 

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Correspondence to Jin-Hee Cho .

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Moore, T.J., Cho, JH. (2019). Applying Percolation Theory. In: Kott, A., Linkov, I. (eds) Cyber Resilience of Systems and Networks. Risk, Systems and Decisions. Springer, Cham. https://doi.org/10.1007/978-3-319-77492-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-77492-3_6

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