A Bio-inspired Approach for Self-protecting an Organic Middleware with Artificial Antibodies

  • Andreas Pietzowski
  • Benjamin Satzger
  • Wolfgang Trumler
  • Theo Ungerer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4124)


Our human body is well protected by antibodies from our biological immune system. This protection system matured over millions of years and has proven its functionality. In our research we are going to transfer some techniques of a biological immune system to a computer based environment. Our goal is to design a self-protecting middleware which is not vulnerable to malicious events. First off this paper proposes an artificial immune system and evaluates optimal parameter settings. This shows the correlation between the size of a system and the length of the receptors used within antibodies for an efficient detection. Our tests showed that the recognition rate of unknown malicious objects can reach up to 99%. Further on we describe the integration of the immune system into our organic middleware OCμ and afterwards we propose techniques to minimize the memory space needed for storing the antibodies and to speedup the time needed for detecting malicious messages. We obtained a space minimization by 30% and gained a speedup of 30 with execution time optimization.


Recognition Rate Message Type Incoming Message Recognition Probability Organic Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Le Boudec, J.-Y., Sarafijanović, S.: An artificial immune system approach to misbehavior detection in mobile ad hoc networks. In: Ijspeert, A.J., Murata, M., Wakamiya, N. (eds.) BioADIT 2004. LNCS, vol. 3141, pp. 396–411. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Burgess, M.: Computer Immunology. In: Twelfth Systems Administration Conference (LISA 1998), Boston, Massachusetts (December 1998)Google Scholar
  3. 3.
    Corless, R.M., Jeffrey, D.J., Knuth, D.E.: A Sequence of Series for the Lambert W Function. In: International Symposium on Symbolic and Algebraic Computation, Maui, Hawaii, USA, pp. 197–204. ACM, New York (1997)CrossRefGoogle Scholar
  4. 4.
    de Castro, L.N., Timmis, J.: Artificial Immune Systems: A Novel Paradigm to Pattern Recognition. In: Corchado, J.M., Alonso, L., Fyfe, C. (eds.) Artificial Neural Networks in Pattern Recognition, SOCO 2002, University of Paisley, UK, pp. 67–84 (2002)Google Scholar
  5. 5.
    de Castro, L.N., von Zuben, F.J.: Biologically Inspired Computing. Idea Group Publishing (2005)Google Scholar
  6. 6.
    D’haeseleer, P.: An Immunological Approach to Change Detection: Theoretical Results. In: 9th IEEE Computer Security Foundations Workshop, Dromquinna Manor, County Kerry, Ireland. IEEE, Los Alamitos (1996)Google Scholar
  7. 7.
    Hall, J.M., Frincke, D.A.: An Architecture for Intrusion Detection Modeled After the Human Immune System. In: Proceedings of the International Conference on Computer, Communication and Control Technologies, vol. 6, pp. 75–78 (2003)Google Scholar
  8. 8.
    Hart, E., Timmis, J.: Application Areas of AIS: The Past, The Present and The Future. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 483–497. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Hofmeyr, S.A., Forrest, S.: Architecture for an Artificial Immune System. Evolutionary Computation 8(4), 45–68 (2000)CrossRefGoogle Scholar
  10. 10.
    Hofmeyr, S.A.: An Immunological Model of Distributed Detection and Its Application to Computer Security. Ph.D thesis, University of New Mexico (May 1999)Google Scholar
  11. 11.
    Ji, Z., Dasgupta, D.: Estimating the Detector Coverage in a Negative Selection Algorithm. In: Genetic and Evolutionary Computation Conference (GECCO 2005), Washington DC, pp. 281–288. ACM, New York (2005)CrossRefGoogle Scholar
  12. 12.
    Kephart, J.O., Chess, D.M.: The Vision of Autonomic Computing, pp. 41–50. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  13. 13.
    Müller-Schloer, C.: Organic Computing Initiative (April 2004), Published as PDF on:
  14. 14.
    Rivest, R.: The MD5 Message-Digest Algorithm. Technical Report Request for Comments: 1321, Internet Engineering Task Force (IETF) (April 1992)Google Scholar
  15. 15.
    Somayaji, A., Hofmeyr, S., Forrest, S.: Principles of a Computer Immune System. In: New Security Paradigms Workshop, Cumbria, UK, pp. 75–82. ACM, New York (1997)CrossRefGoogle Scholar
  16. 16.
    Trumler, W., Bagci, F., Petzold, J., Ungerer, T.: AMUN - autonomic middleware for ubiquitous environments applied to the smart doorplate. Advanced Engineering Informatics (19), 243–252 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andreas Pietzowski
    • 1
  • Benjamin Satzger
    • 1
  • Wolfgang Trumler
    • 1
  • Theo Ungerer
    • 1
  1. 1.Institute of Computer ScienceUniversity of AugsburgAugsburgGermany

Personalised recommendations