摘要
射频指纹利用射频前端的硬件特征作为标识符对设备进行识别。针对现有射频指纹识别研究忽略接收机硬件特性的干扰,导致模型在不同接收机设备上泛化性较差的问题,提出一种基于SE(Squeeze-and-Excitation)注意力多源域对抗网络的射频指纹识别方法。该方法采用多个源域有标签数据和少量目标域无标签数据进行对抗训练以提取与接收机域无关的特征;融合SE注意力机制增强模型对发送机射频指纹特征的学习能力;结合极少量目标域有标签数据对模型参数进行微调,进一步提高发送机识别性能。在Wisig公开数据集上的实验结果表明:该方法在跨接收机场景下可有效识别发送机设备,平均准确率可达83.1%;加入少量有标签数据微调后平均准确率可进一步提高至93.1%。
RF fingerprinting uses the hardware features of RF front-end as identifiers to identify devices.Aiming at the problem that existing RF fingerprinting research ignores the interference of receiver hardware features,resulting in poor generalization of the model on different receiver devices,an RF fingerprinting method based on squeeze and excitation(SE)attention multi-source domain adversarial network is proposed.Multiple source-domain labelled data and a small amount of target-domain unlabelled data are used for adversarial training to extract receiver-domain independent features.Incorporating SE attention mechanism enhances the model’s ability to learn RF fingerprint features from the transmitter.The model parameters are fine-tuned by combining a very small amount of tagged data in the target domain to further improve the performance of transmitter identification.Experimental results on the Wisig dataset show that this method can effectively identify the transmitter device in the cross-receiver scenario,with an average accuracy of up to 83.1%,and the average accuracy can be further improved to 93.1%by adding a small amount of tagged data to fine-tune the model.
作者
苏超然
张大龙
黄勇
董安
SU Chaoran;ZHANG Dalong;HUANG Yong;DONG An(School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450002,China)
出处
《计算机科学》
北大核心
2025年第1期412-419,共8页
Computer Science
基金
国家自然科学基金(62301499)。
关键词
射频指纹识别
多源域对抗
深度学习
物理层安全
SE注意力机制
RF fingerprint recognition
Multi-source domain adversarial
Deep learning
Physical layer security
SE attention
