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Generative Adversarial Network-Based Data Augmentation for Enhancing Wireless Physical Layer Authentication.

Lamia Alhoraibi¹, Daniyal Alghazzawi¹, Reemah Alhebshi¹

¹Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)

|January 26, 2024

Summary

This summary is machine-generated.

This study enhances wireless security by using deep learning for node classification. Generative adversarial networks and convolutional neural networks improved classification accuracy by 19%, addressing dataset limitations.

Keywords:

convolutional neural network generative adversarial networks wireless physical layer authentication

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Area of Science:

Cybersecurity
Machine Learning
Wireless Communication

Background:

Wireless physical layer authentication is crucial for robust wireless security.
Deep learning techniques have shown significant promise in wireless node classification and recognition.
A major challenge is the lack of sufficient datasets for training deep learning models in this domain.

Purpose of the Study:

To develop and evaluate a data-driven approach for wireless node classification using deep learning.
To address the dataset scarcity issue through automated data augmentation.
To improve the accuracy of wireless physical layer authentication models.

Main Methods:

Utilized generative adversarial networks (GANs) for automated data augmentation.
Applied a convolutional neural network (CNN) for wireless node classification.
Compared model performance using an original dataset and a synthetically generated dataset.

Main Results:

The proposed data-driven models demonstrated effective wireless node classification.
Data augmentation using GANs facilitated improved model training.
Achieved an approximate 19% increase in classification accuracy rate compared to baseline.

Conclusions:

Deep learning, particularly with GANs and CNNs, offers a powerful solution for wireless node classification and authentication.
Automated data augmentation is a viable strategy to overcome dataset limitations in wireless security research.
The developed models show significant potential for enhancing wireless security systems.