Fine-Grained Radio Frequency Fingerprint Recognition Network Based on Attention Mechanism

With the rapid development of the internet of things (IoT), hundreds of millions of IoT devices, such as smart home appliances, intelligent-connected vehicles, and wearable devices, have been connected to the network. The open nature of IoT makes it vulnerable to cybersecurity threats. Traditional c...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2024-01, Vol.26 (1), p.29
Main Authors: Zhang, Yulan, Hu, Jun, Jiang, Rundong, Lin, Zengrong, Chen, Zengping
Format: Article
Language:English
Subjects:
Accuracy
attention mechanism
Biometric recognition systems
Biometry
Classification
Communication
Computer vision
Cryptography
Cybersecurity
Deep learning
fine-grained classification
Fingerprint verification
Household appliances
Internet of Things
Machine vision
Methods
Modules
Neural networks
Radio frequency
Radio frequency identification
RF fingerprint
Semantics
Smart buildings
Support vector machines
Wavelet transforms
Wearable technology
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Summary:With the rapid development of the internet of things (IoT), hundreds of millions of IoT devices, such as smart home appliances, intelligent-connected vehicles, and wearable devices, have been connected to the network. The open nature of IoT makes it vulnerable to cybersecurity threats. Traditional cryptography-based encryption methods are not suitable for IoT due to their complexity and high communication overhead requirements. By contrast, RF-fingerprint-based recognition is promising because it is rooted in the inherent non-reproducible hardware defects of the transmitter. However, it still faces the challenges of low inter-class variation and large intra-class variation among RF fingerprints. Inspired by fine-grained recognition in computer vision, we propose a fine-grained RF fingerprint recognition network (FGRFNet) in this article. The network consists of a top-down feature pathway hierarchy to generate pyramidal features, attention modules to locate discriminative regions, and a fusion module to adaptively integrate features from different scales. Experiments demonstrate that the proposed FGRFNet achieves recognition accuracies of 89.8% on 100 ADS-B devices, 99.5% on 54 Zigbee devices, and 83.0% on 25 LoRa devices.
ISSN:1099-4300
1099-4300
DOI:10.3390/e26010029