Advertisement

Intelligent Agents as Cells of Immunological Memory

  • Krzysztof Cetnarowicz
  • Gabriel Rojek
  • Rafał Pokrywka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)

Abstract

Application of mechanisms of immune memory in the computer security domain allows to increase performance of certain class of security systems that are based on detection of attacks without a priori knowledge of attack’s technique. Immune memory should enable the system to memorise once encountered attacks and prevent it together with its consequences in the future. The use of agent technologies gives new possibilities in the management of stored attack’s patterns — patterns of obsolete attacks should be deleted but those of new and frequent should be maintained and generalised. In this paper ideas from agent technology and immune memory domain are introduced into computer security, tested and discussed.

Keywords

False Alarm Intrusion Detection Anomaly Detection System Call Intelligent Agent 
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.

References

  1. 1.
    Hofmeyr, S.A.: An Interpretative Introduction to the Immune System. In: Design Principles for the Immune System and other Distributed Autonomous Systems. Oxford University Press, Oxford (2000) (to appear)Google Scholar
  2. 2.
    Forrest, S., Hofmeyr, S.A., Somayaji, A., Longstaff, T.: A Sense of Self for Unix Processes. In: IEEE Symposium on Security and Privacy, pp. 120–128 (1996)Google Scholar
  3. 3.
    Hofmeyr, S.A., Forrest, S., Somayaji, A.: Intrusion Detection Using Sequences of System Calls. Journal of Computer Security 6, 151–180 (1998)Google Scholar
  4. 4.
    Wierzchoń, S.T.: Sztuczne systemy immunologiczne, EXIT, Warszawa. Teoria i zastosowania (2001) (in polish)Google Scholar
  5. 5.
    Kim, J.W.: Integrating Artificial Immune Algorithms for Intrusion Detection. PhD Thesis, Department of Computer Science, University College London (2002)Google Scholar
  6. 6.
    Bengio, Y.: Markovian Models for Sequential Data. Neural Computing Surveys 2, 129–162 (1999)Google Scholar
  7. 7.
    Ron, D., Singer, Y., Tishby, N.: The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length. Machine Learning 25(2–3), 117–149 (1996)MATHCrossRefGoogle Scholar
  8. 8.
    Axelsson, S.: The Base-Rate Fallacy and the Difficulty of Intrusion Detection. ACM Transactions on Information and System Security (TISSEC) 3, 186–205 (2000)CrossRefGoogle Scholar
  9. 9.
    Tadeusiewicz, R.: Sieci Neuronowe. Wyd. 2, Akademicka Oficyna Wydawnicza RM, Warszawa (1993) (in polish)Google Scholar
  10. 10.
    Data sets (2006), available online at http://www.cs.unm.edu/~immsec/systemcalls.htm

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Krzysztof Cetnarowicz
    • 1
  • Gabriel Rojek
    • 2
  • Rafał Pokrywka
    • 3
  1. 1.Institute of Computer ScienceAGH University of Science and TechnologyKrakówPoland
  2. 2.Department of Computer Science in IndustryAGH University of Science and TechnologyKrakówPoland
  3. 3.IBM SWG LaboratoryKrakówPoland

Personalised recommendations