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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Detection of Access Point Spoofing in the Wi-Fi Fingerprinting Based Positioning.

Juraj Machaj1, Clément Safon2, Slavomír Matúška1

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Summary

This study introduces filtering algorithms to combat Wi-Fi access point spoofing in indoor localization systems. The proposed SFKNN method effectively detects spoofing and reduces positioning errors, enhancing accuracy.

Keywords:
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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Indoor positioning systems (IPS) leverage Wi-Fi signals due to widespread availability and device integration.
  • The unlicensed nature of Wi-Fi spectrum enables malicious access point (AP) spoofing, degrading localization accuracy.
  • Existing spoofing mitigation techniques are not easily applicable to off-the-shelf systems.

Purpose of the Study:

  • To propose and evaluate filtering algorithms for minimizing the impact of AP spoofing on Wi-Fi based indoor localization.
  • To assess the effectiveness of these algorithms when combined with the K-Nearest Neighbors (KNN) localization method.

Main Methods:

  • Development of filtering algorithms, specifically SFKNN (Spoofing Filtered K-Nearest Neighbors).
  • Integration of SFKNN with the KNN localization algorithm.
  • Performance evaluation using the UJIIndoorLoc dataset under two distinct spoofing scenarios (1-10 spoofed APs).

Main Results:

  • The proposed SFKNN demonstrated effective detection of spoofed access points.
  • SFKNN successfully reduced the mean localization error by 2-5 meters.
  • Error reduction was particularly significant with a higher number of spoofed access points.

Conclusions:

  • The developed filtering algorithms offer a viable solution for mitigating AP spoofing in Wi-Fi indoor positioning.
  • SFKNN enhances the robustness and accuracy of localization systems against signal manipulation.
  • This approach improves the reliability of consumer-grade devices for indoor navigation.