A Learning Approach for Physical Layer Authentication Using Adaptive Neural Network
| Autor: | Monson H. Hayes, Jianmei Dai, Xiaoying Qiu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science intrusion detection Feature extraction 0211 other engineering and technologies Cryptography Convolutional neural network 02 engineering and technology 0202 electrical engineering electronic engineering information engineering General Materials Science 021110 strategic defence & security studies Authentication Artificial neural network Universal Software Radio Peripheral business.industry General Engineering Physical layer physical layer security 020206 networking & telecommunications machine learning lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 Computer network Communication channel |
| Zdroj: | IEEE Access, Vol 8, Pp 26139-26149 (2020) |
| ISSN: | 2169-3536 |
| Popis: | In communications, innovative paradigm shifts have emerged in integrating various devices into the network to provide advanced and intelligent services. However, various security threats may occur that may not always be detected using traditional cryptographic techniques. Secure authentication is of paramount importance in modern wireless systems. This paper focusses on robust authentication in a time-varying communication environment where conventional authentication mechanisms are severely limited. We propose an Adaptive Neural Network (ANN) as an intelligent authentication process to improve detection accuracy. Specifically, a Data-Adaptive Matrix (DAM) is designed to track time-varying channel features. By utilizing a convolutional neural network as an intelligent authenticator, the proposed approach integrates deep feature extraction and attack detection, hence, leading to effective physical layer security. To evaluate the system, the ANN is prototyped on a universal software radio peripheral (USRP) and its authentication performance is evaluated in a conference room environment. Experimental results show that the ANN is effective in tackling the challenges of physical layer authentication under interference conditions, and is effective in time-varying environments. |
| Databáze: | OpenAIRE |
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