A security and robustness performance analysis of localization algorithms to signal strength attacks

  • Authors:

  • Yingying Chen;Konstantinos Kleisouris;Xiaoyan Li;Wade Trappe;Richard P. Martin

  • Affiliations:

  • Stevens Institute of Technology, Hoboken, NJ;Rutgers University, Piscataway, NJ;Lafayette College, Easton, PA;Rutgers University, Piscataway, NJ;Rutgers University, Piscataway, NJ

  • Venue:
  • ACM Transactions on Sensor Networks (TOSN)
  • Year:
  • 2009

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Abstract

Recently, it has been noted that localization algorithms that use signal strength are susceptible to noncryptographic attacks, which consequently threatens their viability for sensor applications. In this work, we examine several localization algorithms and evaluate their robustness to attacks where an adversary attenuates or amplifies the signal strength at one or more landmarks. We study both point-based and area-based methods that employ received signal strength for localization, and propose several performance metrics that quantify the estimator's precision, bias, and error, including Hölder metrics, which quantify the variability in position space for a given variability in signal strength space. We then conduct a trace-driven evaluation of a set of representative algorithms, where we measured their performance as we applied attacks on real data from two different buildings. We found the median error degraded gracefully, with a linear response as a function of the attack strength. We also found that area-based algorithms experienced a decrease and a spatial-shift in the returned area under attack, implying that precision increases though bias is introduced for these schemes. Additionally, we observed similar values for the average Hölder metric across most of the algorithms, thereby providing strong experimental evidence that nearly all the algorithms have similar average responses to signal strength attacks with the exception of the Bayesian Networks algorithm.