A Peer-to-Peer Blacklisting Strategy Inspired by Leukocyte-Endothelium Interaction

  • Bruce C. TrapnellJr.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3627)

Abstract

This paper describes a multi-agent strategy for blacklisting malicious nodes in a peer-to-peer network that is inspired by the innate immune system, including the recruitment of leukocytes to the site of an infection in the human body. Agents are based on macrophages, T-cells, and tumor necrosis factor, and exist on network nodes that have properties drawn from vascular endothelial tissue. Here I show that this strategy succeeds in blacklisting malicious nodes from the network using non-specific recruitment. This strategy is sensitive to parameters that affect the recruitment of leukocyte agents to malicious nodes. The strategy can eliminate even a large, uniform distribution of malicious nodes in the network.

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References

  1. 1.
    Abbas, A., Lichtman, A., Pober, J.: Cellular and Molecular Immunology, 4th edn. W.B. Saunders Company, Philadelphia (2000)Google Scholar
  2. 2.
    Babaoglu, O., Meling, H., Montresor, A.: Anthill: A framework for the development of agent-based peerto-peer systems. In: Proceedings of the 22nd International Conference on Distributed Computing Systems (ICDCS), Vienna, Austria, pp. 15–22 (2002)Google Scholar
  3. 3.
    Balakrishnan, R., Ranganathan, K.: Vertex Cuts and Edge Cuts. In: §3.1 in A Textbook of Graph Theory, p. 3. Springer, New York (1999)Google Scholar
  4. 4.
    Di Caro, G., Dorigo, M.: AntNet: Distributed Stigmergetic Control for Communications Networks. Journal of Artificial Intelligence Research 9, 317–365 (1998)MATHGoogle Scholar
  5. 5.
    Dasgupta, P.: Incentive Driven Node Discovery in a Peer-to-Peer Network Using Mobile Intelligent Agents. In: Proceedings of the 7th International Conference on Artificial Intelligence, Las Vegas, June 2003, pp. 750–756 (2003)Google Scholar
  6. 6.
    Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  7. 7.
    Jacob, C., Litorco, J., Lee, L.: Immunity Through Swarms: Agent-Based Simulations of the Human Immune System. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 400–412. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Jelasity, M., Montresor, A., Babaoglu, O.: Detection and removal of malicious peers in gossip-based protocols. In: 2nd Bertinoro Workshop on Future Directions in Distributed Computing: Survivability: Obstacles and Solutions (FuDiCo II: S.O.S.), Bertinoro, Italy (June 2004), invitation only workshop, proceedings online at http://www.cs.utexas.edu/users/lorenzo/sos/
  9. 9.
    Lau, H.Y.K., Wong, V.W.K.: Immunologic Control Framework for Automated Material Handling. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 57–68. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Milojicic, D., Kalogeraki, V., Lukose, R., Nagaraja, K., Pruyne, J., Richard, B., Rollins, S., Xu, Z.: Peer-to-peer computing, HP Technical Report, HPL-2002-57 (2002)Google Scholar
  11. 11.
    Mitchell, M.: An Introduction to Genetic Algorithms, p. 166. MIT Press, Cambridge (2001)Google Scholar
  12. 12.
    Nishiyama, H., Mizoguchi, F.: Design of Security System Based on Immune System. In: Proceedings of the 10th IEEE International Workshop on Enabling Technolgies: Infrastructure for Collaborative Enterprises, Massachusetts, pp. 138–143 (2001)Google Scholar
  13. 13.
    Pang, Y., Yan, Y., Yafei, H., Yiping, Z., Shiyong, Z.: Securing Ad Hoc Networks through mobile agent. In: Proceedings of the 3rd International Conference on Information Security, Shainghai, China, pp. 125–129 (2004)Google Scholar
  14. 14.
    Jelasity, M., Montresor, A., Jesi, G.P.: Peersim Peer-to-Peer Simulator, January 7 (2005), http://peersim.sourceforge.net
  15. 15.
    Sathyanath, S., Sahin, F.: AISIMAM – An Artificial Immune System Based Intelligent Multi Agent Model and its Application to a Mine Detection Problem. In: Proceedings of ICARIS 2002: 1st International Conference on Artificial Immune Systems, University of Kent (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Bruce C. TrapnellJr.
    • 1
  1. 1.TopGun Software, Inc. and the University of MarylandBowie

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