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A New MAC Address Spoofing Detection Technique Based on Random Forests.

Bandar Alotaibi1, Khaled Elleithy2

  • 1Computer Science and Engineering Department, University of Bridgeport, 126 Park Ave, Bridgeport, CT 06604, USA. balotaib@my.bridgeport.edu.

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|March 2, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method to detect Media Access Control (MAC) address spoofing in wireless networks by analyzing received signal strength (RSS). The random forest-based solution achieves high accuracy, outperforming existing techniques.

Keywords:
MAC addressdetectionrandom forestsspoofingwireless local area networkswireless sensor networks

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Area of Science:

  • Computer Science
  • Network Security
  • Wireless Communication

Background:

  • Media Access Control (MAC) addresses in wireless networks are vulnerable to spoofing using readily available hardware.
  • Existing MAC address spoofing detection methods often require protocol modifications or are less effective.

Purpose of the Study:

  • To develop a passive and effective solution for detecting MAC address spoofing in wireless networks.
  • To leverage device-location correlated measurements, specifically Received Signal Strength (RSS), for spoofing detection.

Main Methods:

  • A passive detection solution was developed, requiring no modifications to existing wireless standards or protocols.
  • The solution utilizes an ensemble machine learning method, specifically random forests.
  • Performance was evaluated in a live wireless local area network test-bed with two air monitors as sensors.

Main Results:

  • The proposed solution achieved high detection accuracies: 99.77% at 8-13m, 93.16% at 4-8m, and 88.38% at less than 4m from the victim.
  • Comparative analysis demonstrated that this solution significantly outperforms three previously implemented methods.
  • The system operates passively, ensuring compatibility with current network infrastructures.

Conclusions:

  • Received Signal Strength (RSS) provides a robust, hard-to-spoof metric for detecting MAC address spoofing.
  • The random forest-based approach offers a superior and practical solution for wireless network security against MAC spoofing attacks.