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Exploiting Partial-Packet Information for Reactive Jamming Detection: Studies in UWSN Environment

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7730)

Abstract

Reactive jamming in an underwater sensor network (UWSN) environment is a realistic and very harmful threat. It, typically, affects only a small part of a packet (not the entire one), in order to maintain a low detection probability. Prior works on reactive jamming detection were focused on terrestrial wireless sensor networks (TWSNs), and are limited in their ability to (a) detect it correctly, (b) distinguish the small corrupted part from the uncorrupted part of a packet, and (c) be adaptive with dynamic environment. Further, there is currently a need for a generalized framework for jamming detection that outlines the basic operations governing it. In this paper, we address these research lacunae by broadly designing such a framework for jamming detection, and specifically a detection scheme for reactive jamming. A key characteristic of this work is introducing the concept of partial-packet (PP) in jamming detection. The introduction of such an approach is unique – the existing works rely on holistic packet analysis, which degrades their performance – a fundamental issue that would substantially affect achieving real-time performance. We estimate the probability of high deviation in received signal strength (RSS) using a weak estimation learning scheme, which helps in absorbing the impact of dynamic environment. Finally, we perform CUSUM-test for reactive jamming detection. We evaluate the performance of our proposed scheme through simulation studies in UWSN environment. Results show that, as envisioned, the proposed scheme is capable of accurately detecting reactive jamming in UWSNs, with an accuracy of 100% true detection, while the average detection delay is substantially less.

Keywords

Reactive jamming partial-packet weak estimation CUSUM 

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References

  1. 1.
    Akyildiz, I.F., Pompili, D., Melodia, T.: Underwater acoustic sensor networks: Research challenges. Ad Hoc Networks 3, 257–279 (2005)CrossRefGoogle Scholar
  2. 2.
    Strasser, M., Danev, B., Capkun, S.: Detection of reactive jamming in sensor networks. ACM Transactions on Sensor Networks 7, 1–29 (2010)CrossRefGoogle Scholar
  3. 3.
    Wilhelm, M., Martinovic, I., Schmitt, J.B., Lenders, V.: Short paper: Reactive jamming in wireless networks - how realistic is the threat? In: Proceedings of WiSec, Hamburg, Germany, pp. 47–52 (2011)Google Scholar
  4. 4.
    Domingo, M.: Securing underwater wireless communication networks. IEEE Wireless Communications 18, 22–28 (2011)CrossRefGoogle Scholar
  5. 5.
    Misra, S., Singh, R., Mohan, S.V.R.: Information warfare-worthy jamming attack detection mechanism for wireless sensor networks using a fuzzy inference system. Sensors 10, 3444–3479 (2010)CrossRefGoogle Scholar
  6. 6.
    Xu, W., Trappe, W., Zhang, Y., Wood, T.: The feasibility of launching and detecting jamming attacks in wireless networks. In: Proc. of MobiHoc, pp. 46–57 (2005)Google Scholar
  7. 7.
    Pelechrinis, K., Iliofotou, M., Krishnamurthy, S.V.: Denial of service attacks in wireless networks: The case of jammers. IEEE Communications Surveys & Tutorials 13, 245–257 (2011)CrossRefGoogle Scholar
  8. 8.
    Ganti, R.K., Jayachandran, P., Luo, H., Abdelzaher, T.F.: Datalink streaming in wireless sensor networks. In: Proceedings of SenSys, pp. 209–222 (2006)Google Scholar
  9. 9.
    Jamieson, K., Balakrishnan, H.: PPR: Partial packet recovery for wireless networks. In: Proceedings of SIGCOMM (2007)Google Scholar
  10. 10.
    Oommen, B.J., Rueda, L.: Stochastic learning-based weak estimation of multinomial random variables and its applications to pattern recognition in non-stationary environments. Pattern Recognition 39, 328–341 (2006)zbMATHCrossRefGoogle Scholar
  11. 11.
    Poor, H., Hadjiliadis, O.: Quickest Detection. Cambridge University Press (2008)Google Scholar
  12. 12.
    Mpitziopoulos, A., Gavalas, D., Konstantopoulos, C., Pantziou, G.: A survey on jamming attacks and countermeasures in wireless sensor networks. IEEE Communications Surveys & Tutorials 11, 42–56 (2009)CrossRefGoogle Scholar
  13. 13.
    Xu, W., Ma, K., Trappe, W., Zhang, Y.: Jamming sensor networks: Attacks and defense strategies. IEEE Network 20, 41–47 (2006)CrossRefGoogle Scholar
  14. 14.
    Cagalj, M., Capkun, S., Hubaux, J.P.: Wormhole -based anti-jamming techniques in sensor networks. IEEE Transactions on Mobile Computing 6, 100–114 (2007)CrossRefGoogle Scholar
  15. 15.
    Cakiroglu, M., Ozcerit, A.T.: Jamming detection mechanisms for wireless sensor networks. In: Proceedings of InfoScale, Vico Equense, Italy, pp. 1–8 (2008)Google Scholar
  16. 16.
    Li, M., Koutsopoulos, I., Poovendran, R.: Optimal jamming attack strategies and network defense policies in wireless sensor networks. IEEE Transactions on Mobile Computing 9, 1119–1133 (2010)CrossRefGoogle Scholar
  17. 17.
    Tan, H.P., Diamant, R., Seah, W.K.G., Waldmeyer, M.: A survey of techniques and challenges in underwater localization. Ocean Engineering 38, 1663–1676 (2011)CrossRefGoogle Scholar
  18. 18.
    Erol-Kantarci, M., Mouftah, H.T., Oktug, S.: A survey of architectures and localization techniques for underwater acoustic sensor networks. IEEE Communications Surveys & Tutorials 13, 487–502 (2011)CrossRefGoogle Scholar
  19. 19.
    Xie, P., Zhou, Z., Peng, Z., Yan, H., Hu, T., Cui, J., Shi, Z., Pei, Y., Zhou, S.: Aqua-Sim: an NS-2 based simulator for underwater sensor networks. In: Proceedings of OCEANS, Mississippi, USA, pp. 1–7 (2009)Google Scholar
  20. 20.
    Berkhovskikh, L., Lysanov, Y.: Fundamentals of Ocean Acoustics. Springer (1982)Google Scholar
  21. 21.
    Xie, P., Cui, J.-H., Lao, L.: VBF: Vector-Based Forwarding Protocol for Underwater Sensor Networks. In: Boavida, F., Plagemann, T., Stiller, B., Westphal, C., Monteiro, E. (eds.) NETWORKING 2006. LNCS, vol. 3976, pp. 1216–1221. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.SIT, Indian Institute of Technology KharagpurIndia

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