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Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack.

DongHyun Ko1, Seok-Hwan Choi1, Sungyong Ahn1

  • 1School of Computer Science and Engineering, Pusan National University, Busan KS012, Korea.

Sensors (Basel, Switzerland)
|December 1, 2020
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Summary
This summary is machine-generated.

This study introduces a new MAC spoofing attack that disrupts Wi-Fi indoor localization systems. A novel deep learning method with a random forest filter enhances localization accuracy against this artificial noise.

Keywords:
MAC spoofing attackconvolutional neural networkfingerprintingindoor localizationindoor localization systemrandom forestreceived signal strength

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Area of Science:

  • Computer Science
  • Electrical Engineering

Background:

  • Wi-Fi based indoor localization systems (ILSs) are popular due to their accuracy and lack of extra hardware.
  • Existing noise reduction methods (filtering, deep learning) are ineffective against artificial noise from adversaries.
  • Natural noise impacts ILS accuracy, prompting research into noise mitigation techniques.

Discussion:

  • A novel media access control (MAC) spoofing attack scenario is presented, significantly degrading Wi-Fi ILS prediction accuracy.
  • This attack introduces artificial noise, challenging current ILS performance.
  • The effectiveness of existing noise reduction methods is severely limited under artificial noise conditions.

Key Insights:

  • A new deep learning-based indoor localization method utilizing a random forest (RF)-filter is proposed.
  • This method demonstrates superior prediction accuracy in environments with artificial noise.
  • Experimental results confirm the proposed method outperforms previous techniques against MAC spoofing attacks.

Outlook:

  • Further research into robust ILS against sophisticated adversarial attacks is warranted.
  • The proposed RF-filter integrated deep learning approach offers a promising direction for secure and accurate indoor localization.
  • Development of advanced defense mechanisms against evolving cyber threats in wireless systems is crucial.