Advertisement

Danger Is Ubiquitous: Detecting Malicious Activities in Sensor Networks Using the Dendritic Cell Algorithm

  • Jungwon Kim
  • Peter Bentley
  • Christian Wallenta
  • Mohamed Ahmed
  • Stephen Hailes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4163)

Abstract

There is a list of unique immune features that are currently absent from the existing artificial immune systems and other intelligent paradigms. We argue that some of AIS features can be inherent in an application itself, and thus this type of application would be a more appropriate substrate in which to develop and integrate the benefits brought by AIS. We claim here that sensor networks are such an application area, in which the ideas from AIS can be readily applied. The objective of this paper is to illustrate how closely a Danger Theory based AIS – in particular the Dendritic Cell Algorithm matches the structure and functional requirements of sensor networks. This paper also introduces a new sensor network attack called an Interest Cache Poisoning Attack and discusses how the DCA can be applied to detect this attack.

Keywords

Danger Theory Artificial Immune Systems Sensor Networks Interest Cache Poisoning Attack 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aickelin, U., Bentley, P.J., Cayzer, 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)CrossRefGoogle Scholar
  2. 2.
    Akyildiz, I.F., et al.: A Survey on Sensor Networks. IEEE Communication Magazine, 102–114 (August 2002)Google Scholar
  3. 3.
    Bentley, P.J., Greensmith, J., Ujjin, S.: Two ways to grow tissue for artificial immune systems. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 139–152. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Estrin, D., Cullar, D., Pister, K., Sukhatme, G.: Connecting the Physical World with Pervasive Networks. In: Pervasive Computing, pp. 59–69 (2002)Google Scholar
  5. 5.
    Greensmith, J., Aickelin, U., Cayzer, S.: Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 153–167. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Greensmith, J., Twycross, J., Aickelin, U.: Dendritic Cells for Anomaly Detection. In: Proc. of IEEE Cong. on Evolutionary Computation (CEC 2006), Vancouver, Canada (2006)Google Scholar
  7. 7.
    Greensmith, J., Aickelin, U., Twycross, J.: Articulation and clarification of the dendritic cell algorithm. In: Bersini, H., Carneiro, J. (eds.) ICARIS 2006. LNCS, vol. 4163, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Hart, E., Timmis, J.I.: 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.
    Intanagonwiwat, C., et al.: Directed Diffusion for Wireless Sensor Networking. IEEE/ACM Trans. on Networking 11(1), 2–16 (2003)CrossRefGoogle Scholar
  10. 10.
    Karlof, C., Wagner, D.: Secure routing in wireless sensor networks: attacks and countermeasures. Ad Hoc Networks, 293–315 (2004)Google Scholar
  11. 11.
    Kim, J., Wilson, W.O., Aickelin, U., McLeod, J.: Cooperative Automated Worm Response and Detection ImmuNe ALgorithm(CARDINAL) Inspired by T-Cell Immunity and Tolerance. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 168–181. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Kim, J., et al.: Immune System Approaches to Intrusion Detection – a Review, under reviewGoogle Scholar
  13. 13.
    Matzinger, P.: Tolerance, danger and the extended family. Annual Reviews in Immunology 12, 991–1045 (1994)CrossRefGoogle Scholar
  14. 14.
    Sarafijanovic, S., Le Boudec, J.: An AIS for misbehaviour detection in mobile ad-hoc networks with virtual thymus, clustering, danger signals and memory detectors. In: Proc. of the 2rd Int. Conf. on AIS (ICARIS). LNCS, pp. 342–356. Springer, Heidelberg (2004)Google Scholar
  15. 15.
    Twycross, J., Aickelin, U.: Towards a conceptual framework for innate immunity. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 112–125. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Twycross, J., Aickelin, U.: Libtissue – implementing innate immunity. In: Proc. of the CEC 2006, Vancouver, Canada (to appear, 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jungwon Kim
    • 1
  • Peter Bentley
    • 1
  • Christian Wallenta
    • 1
  • Mohamed Ahmed
    • 1
  • Stephen Hailes
    • 1
  1. 1.Department of Computer ScienceUniversity College LondonLondonU.K.

Personalised recommendations