Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Immune Systems

ICARIS 2004: Artificial Immune Systems pp 161–174Cite as

Towards Danger Theory Based Artificial APC Model: Novel Metaphor for Danger Susceptible Data Codons

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3239))

Abstract

Danger Theory (DT) sets significant shift in viewpoint about the main goal of human immune system (HIS). This viewpoint, though controversial among immunologists, may enable artificial immune systems’ (AIS) researchers to extract benefits of the theory for designing their systems and solving problems confronting with the conventional approach of self-nonself discrimination. Furthering recent pioneering concepts in the field, this paper aims to gain a distinct look for the data that DT based AIS processes. The proposed concept introduces a novel biological metaphor, DASTON, for observing danger susceptible data chunks/points’ combinations. Preliminary analysis for DASTON identification gives hope for exciting future of the idea. It is an initial effort towards Artificial Antigen Presenting Cell (AAPC) modeling. The concept may initiate an argument that might contribute significantly for the body of knowledge in AIS research.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Matzinger, P.: The Danger Model: A Renewed Sense of Self. Science 296, 301–305 (2002)

    Article  Google Scholar 

  2. Matzinger, P.: The Danger Model In Its Historical Context, Scand. J. Immunol. 54, 4–9 (2001)

    Google Scholar 

  3. Gallucci, S., Lolkema, M., Matzinger, P.: Natural Adjuvants: Endogenous Activators of Dendritic Cells. Nature Medicine 5(11), 1249–1255 (1999)

    Article  Google Scholar 

  4. Matzinger, P.: The Real Function of The Immune System, http://cmmg.biosci.wayne.edu/asg/polly.html (Last accessed on 06-04-04)

  5. Matzinger, P.: An Innate sense of danger. Seminars in Immunology 10, 399–415 (1998)

    Article  Google Scholar 

  6. Aickelin, U., Cayzer, S.: The Danger Theory and Its Application to Artificial Immune Systems. In: Proceedings of the International Conference on Artificial Immune Systems (ICARIS 2002), Edinburgh, UK (2002)

    Google Scholar 

  7. Aicklein, U., Bentley, P., Cayser, S., Kim, J., McLeod, J.: Danger Theory: The Link between AIS and IDS. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 147–155. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Hart, E., Ross, P.: Improving SOSDM: Inspirations from the Danger Theory. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 194–203. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  9. Secker, A., Freitas, A.A., Timmis, J.: A Danger Theory Inspired Approach to Web Mining. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 156–167. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  10. Lutz, M.A., Gervais, F., Bernstein, A., Hattel, A.L., Correll, P.H.: STK Receptor Tyrosine Kinase Regulates Susceptibility to Infection with Listeria Monocytogenes. Infection and Immunity 70(1), 416–418 (2002)

    Article  Google Scholar 

  11. Roy, S., Hill, A.V.S., Knox, K., Griffithsand, D., Crook, D.: Association of Common Genetic Variant with Susceptibility to Invasive Pneumococcal Disease. BMJ 324, 1369 (2002)

    Article  Google Scholar 

  12. Goldmann, W.: The Significance of Genetic Control in TSEs. Microbiology-Today 30, 170–171 (2003)

    Google Scholar 

  13. Blackwell, J.: Genetics and Genomics in Infectious Disease, CIMR Research Report (2002), http://www.cimr.cam.ac.uk/resreports/report2002/pdf/blackwell_low.pdf (Last accessed on 06-04-04)

  14. Coussens, P.M., Tooker, B., Nobis, W., Coussens, M.J.: Genetics and Genomics of Susceptibility to Mycobacterial Infections in Cattle, On-line publication on the 2001 IAAFSC web site (2001)

    Google Scholar 

  15. Hill, A.V.: Genetics and Genomics of Infectious Disease Susceptibility. British Medical Bulletin 55(2), 401–413 (1999)

    Article  Google Scholar 

  16. Tavtigian, S.V., et al.: A Candidate Prostate Cancer Susceptibility Gene at Chromosome 17p. Nature Genetics 27, 172–180 (2001)

    Article  Google Scholar 

  17. Casanova, J.-L.: L, Mendelian Susceptibility to Mycobacterial Infection in Man. Swiss Med Weekly 131, 445–454 (2001)

    Google Scholar 

  18. Wang, L., Smith, D., Bot, S., Dellamary, L., Bloom, A., Bot, A.: Noncoding RNA Danger Motifs Bridge Innate and Adaptive Immunity and are Potent Adjuvants for Vaccination. The Journal of Clinical Investigation 110(8), 1175–1184 (2002)

    Google Scholar 

  19. Ishii, K.J., Suzuki, K., Coban, C., Takeshita, F., Itoh, Y., Matoba, H., Kohn, L.D., Klinman, D.M.: Genomic DNA Released by Dying Cells Induces the Maturation of APCs. The Journal of Immunology 167, 2602–2607 (2001)

    Google Scholar 

  20. Brown, B.D., Lillicrap, D.: Dangerous Liaisons: The Role of “Danger” Signals in the Immune Response to Gene Therapy. Blood 100(4), 1133–1139 (2002)

    Article  Google Scholar 

  21. Kowalczyk, D.W.: Tumors and the Danger Model. Acta Biochimica Polonica 49(2), 295–302 (2002)

    Google Scholar 

  22. Nyeo, S.-L., Yang, I.-C.: Codon Distributions In Dna Sequences of Escherichia Coli. Journal of Biological Systems 10(1), 47–60 (2002)

    Article  MATH  Google Scholar 

  23. Vaidyanathan, P.P., Yoon, B.-J.: Digital Filters for Gene Prediction Applications (2002), http://citeseer.nj.nec.com/vaidyanathan02digital (Last sighted on 01-05-2004)

  24. Denny, P., Hopes, E., Gingles, N., Broman, K.W., McPheat, W., Morten, J., Alexander, J., Andrew, P.W., Brown, S.D.M.: A major Locus Conferring Susceptibility to Infection by Streptococcus Pneumoniae in Mice. Mammalian Genome 14, 448–453 (2003)

    Article  Google Scholar 

  25. Branden, C., Tooze, J.: Introduction to Protein Structure. Garland Publishing Inc., New York (1991)

    Google Scholar 

  26. Hofmeyr, S.A., Forrest, S.: Immunity by Design: An Artificial Immune System. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), CA, pp. 1289–1296 (1999)

    Google Scholar 

  27. Hart, E., Ross, P.: Exploiting the Analogy Between Immunology and Sparse Distributed Memories: A System for Clustering Non-stationary data. In: Proc. of International Conference on Artificial Immune Systems (ICARIS 2002), pp. 49–58 (2002)

    Google Scholar 

  28. Forrest, S., et al.: Self Non-self Discrimination in Computer. In: Proceedings of the IEEE Symposium on Research in Security and Privacy, Los Alamitos, CA (1994)

    Google Scholar 

  29. Kephart, J.O.: A Biologically Inspired Immune System for Computer. In: Proceedings of Artificial Life, Cambridge, MA (1994)

    Google Scholar 

  30. Forrest, S., Balthrop, J., Glickman, M., Ackley, D.: Computation in the Wild. In: Park, K., Willins, W. (eds.) the Internet as a Large-Complex System, July 18, Oxford University Press, Oxford (2002)

    Google Scholar 

  31. Glenn Majors, D.: Operating System Call Integrity of the Linux Operating System, Masters Thesis, University of Missouri–Rolla (2003)

    Google Scholar 

  32. Somayaji, A.: Operating System Stability and Security through Process Homeostasis, PhD Dissertation, The University of New Mexico, New Mexico (2002)

    Google Scholar 

  33. Provos, N.: Improving Host Security with System Call Policies, CITI Technical Report 02-3, Center for Information Technology Integration, University of Michigan, Ann Arbor, MI (2002)

    Google Scholar 

  34. Sekar, R., Bendre, M., Bollineni, P., Dhurjati, D.: A Fast Automaton-Based Method for Detecting Anomalous Program Behaviors. In: Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA (2001)

    Google Scholar 

  35. Asaka, M., Onabota, T., Inoue, T., Okazawa, S., Goto, S.: A New Intrusion Detection Method Based on Discriminant Analysis. IEICE Transactions on Information and Systems E84-D(5), 570–577 (2001)

    Google Scholar 

  36. Wagner, D., Dean, D.: Intrusion Detection via Static Analysis. In: Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA (2001)

    Google Scholar 

  37. Li, S., Jones, A.: Temporal Signatures for Intrusion Detection. In: Proc. of the 17th Annual Computer Security Applications Conference, New Orleans, Louisiana (2001)

    Google Scholar 

  38. Warrender, C., Forrest, S., Pearlmutter, B.: Detecting Intrusions Using System Calls: Alternative Data Models. In: Proceedings of the IEEE Symposium on Security and Privacy, pp. 133–145 (1999)

    Google Scholar 

  39. Som, A., Chattopadhyay, S., Chakrabarti, J., Bandyopadhyay, D.: Codon Distributions in DNA. Physics 1 (2001)

    Google Scholar 

  40. Satya, R.V., Mukherjee, A., Ranga, U.: A Pattern Matching Algorithm for Codon Optimization and CpG Motif-Engineering in DNA Expression Vectors. In: Proceedings of the Computational Systems Bioinformatics, CSB 2003 (2003)

    Google Scholar 

  41. Nyeo, S.-L., Yang, A.I.-C.: Codon Distributions In DNA Sequences of Escherichia Coli. Journal Of Biological Systems 10(1), 47–60 (2002)

    Article  MATH  Google Scholar 

  42. Termier, M.: Genome Analysis and Sequences with Random Letter Distribution. In: Proceedings of Algorithms Seminar, pp. 63-66 (2001)

    Google Scholar 

  43. Intrusion Detection Data Sets, http://www.cs.unm.edu/immsec/systemcalls.htm (Last sighted on 01-05-2004)

Download references

Author information

Authors and Affiliations

  1. Group on Artificial Immune Systems N Security (GAINS), Faculty of Computer Science and Information Systems (FSKSM), Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor, Malaysia

    Anjum Iqbal & Mohd Aizani Maarof

Authors
  1. Anjum Iqbal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohd Aizani Maarof
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125, Catania, Italy

    Giuseppe Nicosia  & Vincenzo Cutello  & 

  2. Computer Science Department, University College London, London, UK

    Peter J. Bentley

  3. Department of Electronics, University of York, YO10 5DD, Heslington, York, UK

    Jon Timmis

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Iqbal, A., Maarof, M.A. (2004). Towards Danger Theory Based Artificial APC Model: Novel Metaphor for Danger Susceptible Data Codons. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds) Artificial Immune Systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30220-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30220-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23097-7

  • Online ISBN: 978-3-540-30220-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation