Abstract
In recent years, the field of anonymity and traffic analysis have attracted much research interest. However, the analysis of subsequent dynamics of attack and defense, between an adversary using such topology information gleaned from traffic analysis to mount an attack, and defenders in a network, has recieved very little attention. Often an attacker tries to disconnect a network by destroying nodes or edges, while the defender counters using various resilience mechanisms. Examples include a music industry body attempting to close down a peer-to-peer file-sharing network; medics attempting to halt the spread of an infectious disease by selective vaccination; and a police agency trying to decapitate a terrorist organisation. Albert, Jeong and Barabási famously analysed the static case, and showed that vertex-order attacks are effective against scale-free networks. We extend this work to the dynamic case by developing a framework to explore the interaction of attack and defence strategies. We show, first, that naive defences don’t work against vertex-order attack; second, that defences based on simple redundancy don’t work much better, but that defences based on cliques work well; third, that attacks based on centrality work better against clique defences than vertex-order attacks do; and fourth, that defences based on complex strategies such as delegation plus clique resist centrality attacks better than simple clique defences. Our models thus build a bridge between network analysis and traffic analysis, and provide a framework for analysing defence and attack in networks where topology matters. They suggest definitions of efficiency of attack and defence, and may even explain the evolution of insurgent organisations from networks of cells to a more virtual leadership that facilitates operations rather than directing them. Finally, we draw some conclusions and present possible directions for future research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Albert, R., Barabási, A.L.: Statistical Mechanics of Complex Networks. Reviews of Modern Physics 74, 47 (2002)
Albert, R., Jeong, H., Barabási, A.L.: Error and attack tolerance of complex networks. Nature 406, 387–482 (2000)
Axelrod, R.: The Evolution of Cooperation. Basic Books, New York (1984)
Axelrod, R.: The Complexity of Cooperation. Princeton University Press, Princeton (1997)
Ballester, C., Calvó-Armengol, A., Zenou, Y.: Who’s Who in Crime Networks – Wanted the Key Player, IUI Working Paper Series 617, The Research Institute of Industrial Economics (2004)
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)
Bollobás, B.: Random Graphs. Academic Press, London (1985)
Brandes, U.: A Faster Algorithm for Betweenness Centrality. J. Math. Soc. 25(2), 163–177 (2001)
Carrington, P.J., Scott, J., Wassermann, S.: Models and Methods in Social Network Analysis. Cambridge University Press, Cambridge (2005)
Chaum, D.: The Dining Cryptographers Problem: Unconditional Sender and Recipient Untraceability. Journal of Cryptology 1, 65–75 (1989)
Erdös, P., Renyi, A.: On Random Graphs. Publicationes Mathematicae 6, 290–297 (1959)
Freeman, L.C.: A set of measuring centrality based on betweenness. Sociometry 40, 35–41 (1977)
Jackson, M.O.: A Survey of Models of Network Formation: Stability and Efficiency A. In: Demange, G., Wooders, M. (eds.) Group Formation in Economics: Networks, Clubs, and Coalitions, Cambridge University Press, Cambridge
Krebs, V.E.: Mapping Networks of Terrorist Cells. Connections 12(3), http://www.locative.net/tcmreader/index.php?mapping;krebs
Holme, P., Kim, B.J., Yoon, C.N., Han, S.K.: Attack Vulnerability of Complex Networks. Phys. Rev. E 65, art. no. 018101 (2002)
Milgram, S.: The Small World Problem. Psychology Today 2, 60–87 (1967)
Newmann, M.E.J.: Structure and Function of Complex Networks. SIAM Review 45, 167–256 (2003)
Sparrow, M.K.: The Application of Network Analysis to Criminal Intelligence: An assessment of the prospects. Social Networks 13, 253–274 (1990)
Thompson, N.: The Network Effect – Why Senegal’s bold anti-AIDS program is working. The Boston Globe (January 5, 2003), http://www.newamerica.net/index.cfm?pg=article&DocID=1092
Wassermann, S., Faust, K.: Social Network Analysis. Cambridge University Press, Cambridge (1994)
Watts, D.J.: Six Degrees: The Science of a Connected Age. Norton, New York (2003)
Watts, D.J., Strogatz, S.H.: Collective Dynamics of Small-World Networks. Nature 393, 440–442 (1998)
Zhao, L.A., Park, K.H., Lai, Y.C.: Attack vulnerability of scale-free networks due to cascading breakdown. Physical review E 70, 035101 (R) (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nagaraja, S., Anderson, R. (2008). Dynamic Topologies for Robust Scale-Free Networks. In: Liò, P., Yoneki, E., Crowcroft, J., Verma, D.C. (eds) Bio-Inspired Computing and Communication. BIOWIRE 2007. Lecture Notes in Computer Science, vol 5151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92191-2_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-92191-2_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92190-5
Online ISBN: 978-3-540-92191-2
eBook Packages: Computer ScienceComputer Science (R0)