Advertisement

Design and Implementation of Security System Based on Immune System

  • Hiroyuki Nishiyama
  • Fumio Mizoguchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2609)

Abstract

We design a network security system using an analogy of natural world immunology. We adopt an immune mechanism that distinguishes self or non-self and cooperation among immune cells of the system. This system implements each immune cell as an agent based on our multiagent language, which is an extension of concurrent logic programming languages. These agents can detect and reject intrusion by cooperating with each other.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J. Balthrop, S. Forrest and M. Glickman, Revisiting LISYS: Parameters and Normal Behavior, Proceedings of the 2002 Congress on Evolutionary Computation (in press).Google Scholar
  2. 2.
    W. DuMouchel, Computer intrusion detection based on Bay es Factors for comparing command transition probabilities, National Institut e of Statistical Sciences Technical Report, 1999.Google Scholar
  3. 3.
    S. Forrest, S. A. Hofmeyr and A. Somayaji, Computer Immunology, Communications of the ACM, Vol. 40, No. 10, pp. 88–96, 1997.CrossRefGoogle Scholar
  4. 4.
    S. Forrest, A.S. Perelson, L. Allen and R. Cherukuri, Self-Nonself Discrimination in a Computer, In Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy, 1994.Google Scholar
  5. 5.
    Yumiko Hanaoka, Goichiro Hanaoka and Hideki Imai, Artificial Immune System: A New Model of Anomaly Detection and Its Methods of Implementation, Computer Security Symposium 2000, pp. 231–236, 2000.Google Scholar
  6. 6.
    S. A. Hofmeyr and S. Forrest, Architecture for an artificial immune system, Evolutionary Computation, 7(1), pp. 45–68, 2000.Google Scholar
  7. 7.
    T. Lane and C. E. Brodley, Temporal Sequence Learning and Data Reduction for Anomaly Detection, ACM Transactions on Information and System Security, 2(3), pp. 295–331, 1999.CrossRefGoogle Scholar
  8. 8.
    Fumio Mizoguchi, Anomaly Detection Using Visualization and Machine Learning, Proc. of the Ninth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (Workshop: Enterprise Security), pp. 165–170, 2000.Google Scholar
  9. 9.
    Peter G. Neumann and Phillip A. Porras, Experience with EMERALD to DATE, Usenix Workshop on Intrusion Detecion, 1999.Google Scholar
  10. 10.
    H. Nishiyama, H. Ohwada and F. Mizoguchi, A Multiaget Robot Language for Communication and Concurrency Control, International Conference on Multiagent Systems, pp. 206–213, 1998.Google Scholar
  11. 11.
    Tomio Tada, Semantics of immunology (in Japanease), Seidosha, 1993.Google Scholar
  12. 12.
    A. Taguchi, et al, The Study and Implementation for Tracing Intruder by Mobile Agent, and Intrusion Detection using Marks, Proc. of the 1999 Synposium on Cryptography and Information Security, pp. 627–632, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hiroyuki Nishiyama
    • 1
  • Fumio Mizoguchi
    • 1
  1. 1.Information Media CenterScience University of TokyoNodaJapan

Personalised recommendations