Physical-Layer Authentication Using Channel State Information and Machine Learning
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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. Our approach uses received channel state information to train a neural network in an adversarial setting. These characteristics are then used to maintain authentication in subsequent communication sessions. This work analyzes the use of information from the wireless environment for the purpose of authentication and demonstrates the employment of a generative adversarial neural network trained with received channel data to authenticate a transmitting device without prior knowledge of receiver noise.
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