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)


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.


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.


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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

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