Physical-Layer Authentication Using Channel State Information and Machine Learning

• URL: http://arxiv.org/abs/2006.03695v2
• Date: Mon, 31 Aug 2020 16:36:00 GMT
• Title: Physical-Layer Authentication Using Channel State Information and Machine Learning
• Authors: Ken St. Germain, Frank Kragh
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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