Strong authentication in an interconnected wireless environment continues to be an important, but sometimes elusive goal. Research in physical-layer authentication using channel features holds promise as a technique to improve network security for a variety of devices. We propose the use of machine learning and measured multiple-input multiple-output communications channel information to make a decision on whether or not to authenticate a particular device. This work analyzes the use of received channel state information from the wireless environment and demonstrates the employment of a generative adversarial neural network (GAN) trained with received channel data to authenticate a transmitting device. We compared a variety of machine learning techniques and found that the local outlier factor (LOF) algorithm reached 100% accuracy at lower signal to noise ratios (SNR) than other algorithms. However, before LOF reached 100%, we also show that the GAN was more accurate at lower SNR levels.
@article{arxiv.2006.03695,
title = {Physical-Layer Authentication Using Channel State Information and Machine Learning},
author = {Ken St. Germain and Frank Kragh},
journal= {arXiv preprint arXiv:2006.03695},
year = {2020}
}
Comments
Submitted to 14th International Conference on Signal Processing and Communication Systems (ICSPCS) 2020