Physical-Layer Authentication Using Channel State Information and Machine Learning
This work addresses network security for interconnected wireless devices, presenting an incremental improvement in physical-layer authentication methods.
The paper tackles the problem of strong authentication in wireless networks by using machine learning on channel state information, achieving 100% accuracy with the local outlier factor algorithm at lower signal-to-noise ratios and showing that a generative adversarial neural network is more accurate at even lower SNR levels.
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.