Adaptive RF Fingerprints Fusion via Dual Attention Convolutions

In recent years, with the rise of intelligent hardware technology, the Internet of Things (IoT) has achieved rapid development and has been widely used in smart cities, smart power grids, industrial Internet, and other fields. The resulting security risks of IoT have attracted more and more attention. Being different from the traditional authentication that is based on MAC or security certificate, RF fingerprinting technology extracts fingerprints from the emissions of wireless transmitters. These fingerprints root in the hardware imperfection of the transmitting circuits and can be used for wireless device identification and authentication. RF fingerprinting can effectively enhance the security of the wireless network and has become a research focus in the field of IoT. However, how to learn and fuse multiple RF fingerprints is still an urgent problem to be solved. Inspired by the attention mechanism in the field of computer vision, an adaptive RF fingerprints fusion network (ARFNet) is proposed in this article, which is built on our dual attention convolution (DAConv) layer. This neural network can extract and adaptively fuse multiple RF fingerprints in a data-driven manner to obtain more discriminant features. In addition, a data augmentation method is also designed to effectively enhance the network's robustness to channel variation and SNR variation. Extensive experimental results show that the proposed ARFNet combined with data augmentation can achieve 99.5% recognition accuracy on five USRP X310, and achieve 95.7% recognition accuracy on 56 ADS-B devices. Our source code has been released at https://github.com/zhangweifeng1218/Adaptive_RF_Fingerprinting .