Genetic Algorithm Approach for Non-self OS Process Identification

Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 259)

Abstract

Computers have proved to be an inevitable part of our modern life. Every work of our modern life involve directly or indirectly the use of computers. A lot of our personal as well as confidential work related information is stored in computer systems. Therefore, it is an important task to secure this information and protect it. In this paper, the aim is to establish the sense in computer system that could differentiate between the self process (i.e. processes that are not harmful to our computer system) and the non-self process (i.e. processes that are harmful and dangerous to our computer system). A process coming in the system is identified whether the process is part of the stable system i.e. self process or is it a harmful process which can destabilize a system i.e. non-self process. This is done with the help of the detectors generated by the genetic algorithm. This technique would be used to classify the processes at process level into SELF (non-harmful) and NON-SELF (harmful or dangerous). This would help the system to sense the processes before the harmful processes do any harm to the system.

Keywords

Artificial immune system Genetic algorithm Self Non-self Computer security Detector 
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References

  1. 1.
    Hofmeyr, S.A., Forrest, S.: Architecture of an artificial immune system. Evol. Comput. 8(4), 443–473 (2000). doi: 10.1162/106365600568257 CrossRefGoogle Scholar
  2. 2.
    Spafford, E.H.: Computer viruses as artificial life. J. Artif. Life 1(3), 249–265 (1994). (http://spaf.cerias.purdue.edu/tech-reps/985.pdf) (MIT Press)Google Scholar
  3. 3.
    Dasgupta, D. (ed.): Artificial Immune Systems and their Applications. Springer, Berlin (1999)MATHGoogle Scholar
  4. 4.
    Lau, L.W.Y.: Computer immunology, The Department of Information Technology and Electrical Engineering, The University of Queensland. http://download.adamas.ai/dlbase/ebooks/VX_related/Computer%20Immunology(Lau).pdf (2002)
  5. 5.
    Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system, adaptation and machine learning. In: Proceedings of the Fifth Annual International Conference in Physica D: Nonlinear Phenomena, vol. 22, no. 1–3, pp. 187–204. Elsevier, Amsterdam (1986)Google Scholar
  6. 6.
    Bachmayer, S. (eds.): Artificial immune system. In: ch XI, pp. 209–230. Group Idea Publishing Hershey (2006) http://www.cs.helsinki.fi/u/niklande/opetus/SemK07/paper/bachmayer.pdf
  7. 7.
    Carter, J.H.: The immune system as a model for pattern recognition and classification. J. Am. Med. Inf. Assoc. 7(3), 28–41(2000)Google Scholar
  8. 8.
    Percus, J.K., Percus, O.E., Perelson, A.S.: Predicting the size of the antibody-combining region from consideration of efficient self/nonself discrimination. In: Proceedings of the National Academy of Science, vol. 90, pp. 1691–1695. National Acad Sciences (1993)Google Scholar
  9. 9.
    Forrest, S., Hofmeyr, S.A., Somayaji, A.B., Longstaff, T.A.: A sense of self for UNIX processes, submitted to the IEEE Symposium on Security and Privacy (1996)Google Scholar
  10. 10.
    Forrest, S., Perelson, A.S.: Self non-self discrimination in a computer. In: Proceedings of the IEEE Symposium on Research in Security and Privacy IEEE (1994)Google Scholar
  11. 11.
    Niu, J.: Process Description and Control, CSc33200: Operating Systems, CS-CCNY, Fall. http://www.sci.brooklyn.cuny.edu/~jniu/teaching/csc33200/csc33200.html (2003)
  12. 12.
    Percus, J.K., Percus, O.E., Perelson, A.S.: Probability of self-nonself discrimination. In: Theoretical and Experimental Insights into Immunology, series vol. 66, pp. 63–70. NATO ASI (1992)Google Scholar
  13. 13.
    Eiben, A.E, Raue, P.E., Ruttkay, Z.: Genetic algorithms with multi-parent recombination. In: PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature, vol. 866, pp. 78–87. Springer, Berlin (1994)Google Scholar
  14. 14.
    Rojas, R.: Genetic Algorithm from Neutral Networks: A Systematic Introduction, pp. 427–448. Springer, Berlin (1996)Google Scholar
  15. 15.
    Dal, D., Abraham, S., Abraham, A., Sanyal, S, Sanglikar, M.: Evolution induced secondary immunity: an artificial immune system based intrusion detection system. IEEE 7th Computer Information Systems and Industrial Management Applications. doi:  10.1109/CISIM.2008.31 pp. 65–70. IEEE (2008)
  16. 16.
    Darmoul, S., Pierrevalt, H., Gabouj, S.H.: scheduling using artificial immune system metaphors: a review. In: Proceedings of the International Conference on Service Systems and Service Management, pp. 1150–1155. IEEE (2006)Google Scholar
  17. 17.
    Matloff, N.: Unix processes, Department of Computer Science University of California at Davis. http://heather.cs.ucdavis.edu/~matloff/unix.html (2004)

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Jaypee University of Engineering and TechnologyGunaIndia

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