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Outlier detection and countermeasure for hierarchical wireless sensor networks

Outlier detection and countermeasure for hierarchical wireless sensor networks

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Outliers in wireless sensor networks (WSNs) are sensor nodes that issue attacks by abnormal behaviours and fake message dissemination. However, existing cryptographic techniques are hard to detect these inside attacks, which cause outlier recognition a critical and challenging issue for reliable and secure data dissemination in WSNs. To efficiently identify and isolate outliers, this study presents a novel outlier detection and countermeasure scheme (ODCS), which consists of three mechanisms: (i) abnormal event observation mechanism for network surveillance; (ii) exceptional message supervision mechanism for distinguishing fake messages by exploiting spatiotemporal correlation and consistency and (iii) abnormal behaviour supervision mechanism for the evaluation of node behaviour. The ODCS provides a heuristic methodology and does not need the knowledge about normal or malicious sensors in advance. This property makes the ODCS not only to distinguish and deal with various dynamic attacks automatically without advance learning, but also to reduce the requirement of capability for constrained nodes. In the ODCS, the communication is limited in a local range, such as one-hop or a cluster, which can reduce the communication frequency and circumscribe the session range further. Moreover, the ODCS provides countermeasures for different types of attacks, such as the rerouting scheme and the rekey security scheme, which can separate outliers from normal sensors and enhance the robustness of network, even when some nodes are compromised by adversary. Simulation results indicate that our approach can effectively detect and defend the outlier attack.

References

    1. 1)
    2. 2)
      • Aggarwal, C.C., Yu, P.S.: `Outlier detection for high dimensional data', SIGMOD'01, 2001, New York, NY, USA, p. 37–46.
    3. 3)
      • J. Deng , R. Han , S. Mishra . INSENS: intrusion-tolerant routing for wireless sensor networks. Elsevier J. Comput. Commun. , 2 , 216 - 230
    4. 4)
      • Strehl, J.G., Mooney, R.: `Impact of similarity measures on web-page clustering', Proc. 7th Natl. Conf. on Artificial Intelligence: Workshop of Artificial Intelligence for Web Search, 2000, Austin, Texas, p. 58–64.
    5. 5)
      • Luk, M., Mezzour, G., Perrig, A., Gligor, V.: `MiniSec: a secure sensor network communication architecture', Proc. Sixth Int. Conf. on Information Processing in Sensor Networks (IPSN 2007), April 2007, Cambridge, Massachusetts, p. 479–488.
    6. 6)
      • G. Shakhnarovich , T. Darrel , P. Indyk . (2005) Nearest-neighbor methods in learning and vision.
    7. 7)
      • Liu, F., Cheng, X.Z., Chen, D.C.: `Insider attacker detection in wireless sensor networks', INFOCOM 2007. 26th IEEE Int. Conf. on Computer Communications, May 2007, Anchorage, Alaska, USA, p. 1937–1945.
    8. 8)
      • Zhu, S., Setia, S., Jajodia, S.: `LEAP: efficient security mechanisms for large-scale distributed sensor networks', Proc. 10th ACM Conf. on Computer and Communications Security, 2003, New York, p. 62–72.
    9. 9)
      • Shu, L., Zhang, Y., Zhou, Z., Hauswirth, M., Yu, Z., Hynes, G.: `Transmitting and gathering streaming data in wireless multimedia sensor networks within expected network lifetime', Fifth Int. Conf. on Ubiquitous Intelligence and Computing (UIC 2008), 23–25 June 2008, Oslo, Norway, p. 306–322.
    10. 10)
      • Leonardo, B., Oliveira Hao, C., Wong, M.: `SecLEACH: a random key distribution solution for securing clustered sensor networks', Fifth IEEE Int. Symp. on Network Computing and Applications, 2006, Washington, DC, USA, p. 145–154.
    11. 11)
      • Zhang, Y., Lee, W.: `Intrusion detection in wireless ad-hoc networks', ACM MOBICOM 2000, August 2000, Boston, Massachusetts, USA, p. 275–283.
    12. 12)
      • Knorr, E.M., Ng, R.T.: `Finding intensional knowledge of distance-based outliers', VLDB'99, 1999, San Francisco, CA, USA, p. 211–222.
    13. 13)
      • Sanjay, R., Huirong, F., Manohar, S., John, D., Kendall, N.: `Prevention of cooperative black hole attack in wireless ad hoc networks', Proc. 2003 Int. Conf. on Wireless Networks (ICWN'03), Las Vegas, Nevada, USA, p. 570–575.
    14. 14)
      • T. Banerjeea , B. Xiea , D.P. Agrawal . Fault tolerant multiple event detection in a wireless sensor network. J. Parallel Distrib. Comput. , 9 , 1222 - 1234
    15. 15)
      • Branch, J.W., Szymanski, B.K., Giannella, C., Wolff, R., Kargupta, H.: `In-network outlier detection in wireless sensor networks', IEEE ICDCS'06, July 2006, Lisboa, Portugal, p. 51–58.
    16. 16)
      • Silva, A.P., Martins, M.H., Rocha, B.P., Loureiro, A.A., Ruiz, L.B., Wong, H.C.: `Decentralized intrusion detection in wireless sensor networks', ACM Q2SWinet'05, 2005, Montreal, Quebec, Canada, p. 16–23.
    17. 17)
      • Liu, M.J.: `Studies on knowledge discovery methods', 2001, PhD, of Nankai University, (in Chinese).
    18. 18)
      • Zhang, Y.Y., Yang, W.C., Kim, K.B., Cui, M.Y., Park, M.S.: `A Rekey-boosted security protocol in hierarchical wireless sensor network', 2008 Int. Conf. on Multimedia and Ubiquitous Engineering (MUE), 2008, Busan, Korea, p. 57–61.
    19. 19)
      • Abdalla, M., Bellare, M.: `Increasing the lifetime of a key: a comparative analysis of the security of re-keying techniques', Proc. Asiacrypt 2000, December 2000, Kyoto, Japan, p. 546–559, (LNCS, 1976).
    20. 20)
      • Bay, S.D., Schwabacher, M.: `Mining distance-based outliers in near linear time with randomization and a simple pruning rule', KDD'03, 2003, New York, NY, USA, p. 29–38.
    21. 21)
      • Manjeshwar, A., Grawal, D.P.: `TEEN: a protocol for enhanced efficiency in wireless sensor networks', Proc. 15th Parallel and Distributed Processing Symp. on IEEE Computer Society, 2001, San Francisco, p. 2009–2015.
    22. 22)
      • Chang, R.S., Kuo, C.J.: `An energy-efficient routing mechanism for wireless sensor networks', Advanced Information Networking and Applications (AINA'06), IEEE, 18–20 April 2006, 2, p. 5.
    23. 23)
      • Chan, H., Perrig, A., Song, D.: `Random key predistribution schemes for sensor networks', Proc. IEEE Symp. on Security and Privacy, May 2003, Berkeley, CA, USA, p. 197–213.
    24. 24)
      • Ferreira, A.C., Vilac, M.A., Oliveira, L.B., Habib, E., Wong, H.C., Loureiro, A.A.F.: `On the security of cluster based communication protocols for wireless sensor networks', 4thIEEE Int. Conf. on Networking (ICN'05), p. 449–458, Reunion Islan, April 2005, (LNCS, 3420).
    25. 25)
    26. 26)
      • Bandyopadhyay, S., Coyle, E.J.: `An energy efficient hierarchical clustering algorithm for wireless sensor networks', Proc. IEEE INFOCOM'03, April 2003, San Francisco, USA.
    27. 27)
      • M. David , J. Tax , D. Robert , D.D. Ridder . (2004) Classification, parameter estimation and state estimation: an engineering approach using MATLAB.
    28. 28)
      • D.M. Hawkins . (1980) Identification of outliers.
    29. 29)
      • Jamieson, K., Balakrishnan, H., Tay, Y.C.: `Sift: a MAC protocol for event-driven wireless sensor networks', Proc. Third European Workshop on Wireless Sensor Networks (EWSN), February 2006, ETH Zurich, Switzerland, p. 260–275.
    30. 30)
      • Lazarevic, A., Kumar, V.: `Feature bagging for outlier detection', KDD'05, 2005, New York, NY, USA, p. 157–166.
    31. 31)
    32. 32)
      • Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: `LOF: identifying density-based local outliers', SIGMOD'00, 2000, New York, NY, USA, p. 93–104.
    33. 33)
      • J.L. Richard , L. Morris . (2000) Marx: an introduction to mathematical statistics and its applications.
    34. 34)
      • MacQueen, J.B.: `Some methods for classification and analysis of multivariate observations', Proc. Fifth Berkeley Symp. on Mathematical Statistics and Probability, University of California Press, 1967, 1, p. 281–297.
    35. 35)
      • Ramaswamy, S., Rastogi, R., Shim, K.: `Efficient algorithms for mining outliers from large data sets', SIGMOD'00, 2000, New York, NY, USA, p. 427–438.
    36. 36)
    37. 37)
      • Janakiram, D., Reddy, V.A., Kumar, A.V.U.P.: `Outlier detection in wireless sensor networks using Bayesian belief networks', First Int. Conf. on Communication System Software and Middleware, 2006, New Delhi, India, p. 1–6.
    38. 38)
      • Zhuang, Y., Chen, L.: `In-network outlier cleaning for data collection in sensor networks', Proc. 1st Int. VLDB Workshop on Clean Databases (CleanDB'06), September 2006, Seoul, Korea, p. 41–48.
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