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Wireless Local Area Networks Threat Detection Using 1D-CNN.

Marek Natkaniec1, Marcin Bednarz1

  • 1Institute of Telecommunications, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland.

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This study introduces a machine learning algorithm for detecting Layer 2 threats in Wireless Local Area Networks (WLANs). The deep neural network approach enhances security by identifying malicious traffic patterns, improving Wireless Intrusion Detection Systems (WIDS).

Keywords:
MAC layer threatsconvolutional neural networkdeep learningmachine learningnetwork traffic analysisthreat detection

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Area of Science:

  • Computer Science
  • Network Security
  • Machine Learning

Background:

  • Wireless Local Area Networks (WLANs) offer convenient network access but face increasing security threats like jamming and injection attacks.
  • Existing security measures struggle to effectively detect sophisticated Layer 2 threats in dynamic WLAN environments.

Purpose of the Study:

  • To propose and evaluate a novel machine learning algorithm for detecting Layer 2 threats in WLANs.
  • To enhance the capabilities of Wireless Intrusion Detection Systems (WIDS) through advanced network traffic analysis.

Main Methods:

  • Utilized a deep neural network (DNN) model for analyzing network traffic patterns.
  • Developed a robust dataset, including preprocessing and data division for training and testing the DNN.
  • Implemented and tested the proposed algorithm against various Layer 2 attack vectors.

Main Results:

  • The machine learning algorithm demonstrated high accuracy in identifying malicious activity patterns.
  • Experimental results show superior performance compared to existing methods, particularly in terms of precision.
  • The proposed approach effectively detects a range of Layer 2 threats within WLANs.

Conclusions:

  • The developed machine learning algorithm is effective for detecting Layer 2 threats in WLANs.
  • This solution can be integrated into Wireless Intrusion Detection Systems (WIDS) to significantly bolster network security.
  • The findings contribute to more secure and reliable wireless network infrastructures.