@Article{cmc.2025.061869,
AUTHOR = {Xiaoying Qiu, Xiaoyu Ma, Guangxu Zhao, Jinwei Yu, Wenbao Jiang, Zhaozhong Guo, Maozhi Xu},
TITLE = {A Lightweight Convolutional Neural Network with Squeeze and Excitation Module for Security Authentication Using Wireless Channel},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {83},
YEAR = {2025},
NUMBER = {2},
PAGES = {2025--2040},
URL = {http://www.techscience.com/cmc/v83n2/60564},
ISSN = {1546-2226},
ABSTRACT = {Physical layer authentication (PLA) in the context of the Internet of Things (IoT) has gained significant attention. Compared with traditional encryption and blockchain technologies, PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself. Some existing PLA solutions rely on static mechanisms, which are insufficient to address the authentication challenges in fifth generation (5G) and beyond wireless networks. Additionally, with the massive increase in mobile device access, the communication security of the IoT is vulnerable to spoofing attacks. To overcome the above challenges, this paper proposes a lightweight deep convolutional neural network (CNN) equipped with squeeze and excitation module (SE module) in dynamic wireless environments, namely SE-ConvNet. To be more specific, a convolution factorization is developed to reduce the complexity of PLA models based on deep learning. Moreover, an SE module is designed in the deep CNN to enhance useful features and maximize authentication accuracy. Compared with the existing solutions, the proposed SE-ConvNet enabled PLA scheme performs excellently in mobile and time-varying wireless environments while maintaining lower computational complexity.},
DOI = {10.32604/cmc.2025.061869}
}