Advertisement

Robust Wireless Localization: Attacks and Defenses

  • Yanyong Zhang
  • Wade Trappe
  • Zang Li
  • Manali Joglekar
  • Badri Nath
Part of the Advances in Information Security book series (ADIS, volume 30)

Abstract

Many sensor applications are being developed that require the location of wireless devices, and localization schemes have been developed to meet this need. However, as location-based services become more prevalent, the localization infrastructure will become the target of malicious attacks. These attacks will not be conventional security threats, but rather threats that adversely affect the ability of localization schemes to provide trustworthy location information. This paper identifies a list of attacks that are unique to localization algorithms. Since these attacks are diverse in nature, and there may be many unforseen attacks that can bypass traditional security countermeasures, it is desirable to incorporate an additional layer in the data path to classify/clean the corrupted location data. To address these attacks, we outline a general framework for validating location information through data classification and data cleansing techniques. Consistency checking methods can be used to verify that physical measurements are consistent with each other and with physical reality. We then explore more powerful techniques that employ robust statistical methods to make localization schemes attack-tolerant. We examine two broad classes of localization: triangulation and RF-based fingerprinting methods. For triangulation-based localization, we propose an adaptive least squares and least median squares position estimator that has the computational advantages of least squares in the absence of attacks and is capable of switching to a robust mode when being attacked. We introduce robustness to fingerprinting localization through the use of a median-based distance metric. We evaluate our robust localization schemes under different threat conditions.

Keywords

Sensor Network Sensor Node Signal Strength Receive Signal Strength Localization Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    K. Langendoen and N. Reijers, “Distributed localization in wireless sensor networks: a quantitative comparison,” Comput. Networks, vol. 43, no. 4, pp. 499–518, 2003.zbMATHCrossRefGoogle Scholar
  2. 2.
    N. Priyantha, A. Chakraborty, and H. Balakrishnan, “The CRICKET location-support system,” in Proceedings of the 6th annual international conference on Mobile computing and networking (Mobicom 2000), 2000, pp. 32–43.Google Scholar
  3. 3.
    D. Nicelescu and B. Nath, “Ad hoc positioning (APS) using AOA,” in Proceedings of IEEE Infocom 2003, 2003, pp. 1734–1743.Google Scholar
  4. 4.
    S. Capkun and J.P. Hubaux, “Secure positioning in sensor networks,” Technical report EPFL/IC/200444, May 2004.Google Scholar
  5. 5.
    L. Lazos and R. Poovendran, “SeRLoc: Secure range-independent localization for wireless sensor networks,” in Proceedings of the 2004 ACM Workshop on Wireless Security, 2004, pp. 21–30.Google Scholar
  6. 6.
    B. H. Wellenhoff, H. Lichtenegger, and J. Collins, Global Positions System: Theory and Practice, Fourth Edition, Springer Verlag, 1997.Google Scholar
  7. 7.
    A. Harter, A. Hopper, P. Steggles, A. Ward, and P. Webster, “The anatomy of a context-aware application,” in Proceedings of the MOBICOM 99, 1999.Google Scholar
  8. 8.
    A. Savvides, C. C. Han, and M. B. Srivastava, “Dynamic fine-grained localization in ad-hoc networks of sensors,” in Proceedings of the MOBICOM 01, 2001.Google Scholar
  9. 9.
    C. Savarese, K. Langendoen, and J. Rabaey, “Robust positioning algorithms for distributed ad-hoc wireless sensor networks,” in Proceedings of USENIX Technical Annual Conference, 2002.Google Scholar
  10. 10.
    D. Nicelescu and B. Nath, “DV based positioning in ad hoc networks,” Telecommunication Systems, vol. 22, no. 1–4, pp. 267–280, 2003.CrossRefGoogle Scholar
  11. 11.
    A. Savvides, H. Park, and M. Srivastava, “The bits and flops of the n-hop multilateration primitive for node localization problems,” in Proceedings of First ACM International Workshop on Wireless Sensor Networks and Application (WSNA), 2002, pp. 112–121.Google Scholar
  12. 12.
    J. Hightower, G. Boriello, and R. Want, “SpotON: An indoor 3D Location Sensing Technology Based on RF Signal Strength,” Tech. Rep. Technical Report 2000-02-02, University of Washington, February 2000.Google Scholar
  13. 13.
    N. Bulusu, J. Heidemann, and D. Estrin, “Gps-less low cost outdoor localization for very small devices,” IEEE Personal Communications Magazine, vol. 7, no. 5, pp. 28–34, 2000.CrossRefGoogle Scholar
  14. 14.
    M. Youssef, A. Agrawal, and A. U. Shankar, “WLAN location determination via clustering and probability distributions,” in Proceedings of IEEE PerCom’03, Fort Worth, TX, Mar. 2003.Google Scholar
  15. 15.
    T. Roos, P. Myllymaki, and H. Tirri, “A statistical modeling approach to location estimation,” IEEE Transactions on Mobile Computing, vol. 1, pp. 59–69, 2002.CrossRefGoogle Scholar
  16. 16.
    R. Battiti, M. Brunato, and A. Villani, “Statistical learning theory for location fingerprinting in wireless lans,” Technical Report DIT-02-086, University of Trento, Informatica e Telecomunicazioni, Oct. 2002.Google Scholar
  17. 17.
    P. Bahl and V.N. Padmanabhan, “RADAR: An in-building RF-based user location and tracking system,” in Proceedings of IEEE Infocom 2000, 2000, pp. 775–784.Google Scholar
  18. 18.
    E. Elnahrawy, X. Li, and R. P. Martin, “The limits of localization using signal strength: A comparative study,” in Proceedings of the First IEEE International Conference on Sensor and Ad hoc Communations and Networks (SECON 2004), Oct. 2004.Google Scholar
  19. 19.
    T. He, C. Huang, B. Blum, J. Stankovic, and T. Abdelzaher, “Range-free localization schemes for large scale sensor networks,” in Proceedings of the 9th annual international conference on Mobile computing and networking (Mobicom 2003), 2003, pp. 81–95.Google Scholar
  20. 20.
    Y.C. Hu, A. Perrig, and D. Johnson, “Packet leashes: a defense against wormhole attacks in wireless networks,” in Proceedings of IEEE Infocom 2003, 2003, pp. 1976–1986.Google Scholar
  21. 21.
    S. Brands and D. Chaum, “Distance-bounding protocols,” in Proceedings of the Workshop on the Theory and Application of Cryptographic Techniques on Advances in Cryptology, 1994, pp. 344–359.Google Scholar
  22. 22.
    N. Sastry, U. Shankar, and D. Wagner, “Secure verification of location claims,” in Proceedings of the 2003 ACM workshop on Wireless security, 2003, pp. 1–10.Google Scholar
  23. 23.
    S. Capkun and J. P. Hubaux, “Secure positioning of wireless devices with application to sensor networks,” in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), 2005.Google Scholar
  24. 24.
    L. Lazos, R. Poovendran, and S. Capkun, “Rope: robust position estimation in wireless sensor networks,” in Proceedings of the Fourth International Symposium on Information Processing in Sensor Networks (IPSN 2005), 2005, pp. 324–331.Google Scholar
  25. 25.
    S. Capkun and J.P. Hubaux, “Securing localization with hidden and mobile base stations,” to appear in Proceedings of IEEE Infocom 2006.Google Scholar
  26. 26.
    Z. Li, W. Trappe, Y. Zhang, and B. Nath, “Robust statistical methods for securing wireless localization in sensor networks,” in Proceedings of the Fourth International Symposium on Information Processing in Sensor Networks (IPSN 2005), 2005.Google Scholar
  27. 27.
    P. Bahl, V.N. Padmanabhan, and A. Balachandran, “Enhancements to the RADAR User Location and Tracking System,” Tech. Rep. Technical Report MSR-TR-2000-12, Microsoft Research, February 2000.Google Scholar
  28. 28.
    W. Starlings, Network Security Essentials, Applications and Standards, 2nd Edition, Prentice Hall, 2003.Google Scholar
  29. 29.
    C. Kaufman, R. Perlman, and M. Speciner, Network Security: Private Communication in a Public World, Prentice Hall, 1995.Google Scholar
  30. 30.
    W. Trappe and L.C. Washington, Introduction to Cryptography with Coding Theory, Prentice Hall, 2002.Google Scholar
  31. 31.
    D. Niculescu and B. Nath, “VOR base stations for indoor 802.11 positioning,” in Proceedings of the Mobicom 2004, Philadelphia, PA, September 2004.Google Scholar
  32. 32.
    B. Przydatek, D. Song, and A. Perrig, “SIA: secure information aggregation in sensor networks,” in SenSys’ 03: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, 2003, pp. 255–265.Google Scholar
  33. 33.
    D. Wagner, “Resilient aggregation in sensor networks,” in SASN’ 04: Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks, 2004, pp. 78–87.Google Scholar
  34. 34.
    P. Rousseeuw and A. Leroy, “Robust regression and outlier detection,” Wiley-Interscience, September 2003.Google Scholar
  35. 35.
    A. Goldsmith, Wireless Communications, Cambridge University Press, to appear 2005.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Yanyong Zhang
    • 1
  • Wade Trappe
    • 1
  • Zang Li
    • 1
  • Manali Joglekar
    • 1
  • Badri Nath
    • 2
  1. 1.WINLABRutgers UniversityUSA
  2. 2.Computer Science DepartmentRutgers UniversityUSA

Personalised recommendations