Deep learning based RF fingerprinting for device identification and wireless security
| Autor: | Daniel Kuzmenko, Zhou Yu, Carlos Feres, Xin Liu, Xiaoguang ‘Leo’ Liu, Ding Zhi, Qingyang Wu |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Scheme (programming language)
Electrical & Electronic Engineering Computer science short-term memory classifying transmitters device identification 02 engineering and technology RF fingerprinting recurrent neural nets Electrical And Electronic Engineering 0202 electrical engineering electronic engineering information engineering Wireless Electrical and Electronic Engineering important applications computer.programming_language emerging technology Communications Technologies learning Artificial neural network business.industry Noise (signal processing) Deep learning wireless transmitters 020208 electrical & electronic engineering Neurosciences deep learning 020206 networking & telecommunications hardware-specific features Wireless security Artificial Intelligence And Image Processing Identification (information) neural nets Recurrent neural network experimental studies identical RF transmitters deep neural networks recurrent neural network Artificial intelligence business computer Computer hardware wireless security |
| Zdroj: | Wu, Q; Feres, C; Kuzmenko, D; Zhi, D; Yu, Z; Liu, X; et al.(2018). Deep learning based RF fingerprinting for device identification and wireless security. Electronics Letters, 54(24), 1405-1407. doi: 10.1049/el.2018.6404. UC Davis: Retrieved from: http://www.escholarship.org/uc/item/9cz3w5dg Electronics Letters, vol 54, iss 24 |
| DOI: | 10.1049/el.2018.6404. |
| Popis: | Author(s): Wu, Q; Feres, C; Kuzmenko, D; Zhi, D; Yu, Z; Liu, X; Liu, X | Abstract: RF fingerprinting is an emerging technology for identifying hardware-specific features of wireless transmitters and may find important applications in wireless security. In this study, the authors present a new RF fingerprinting scheme using deep neural networks. In particular, a long short-term memory based recurrent neural network is proposed and used for automatically identifying hardware-specific features and classifying transmitters. Experimental studies using identical RF transmitters showed very high detection accuracy in the presence of strong noise (signal-to-noise ratio as low as −12 dB) and demonstrated the effectiveness of the proposed scheme. |
| Databáze: | OpenAIRE |
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