EML-PSP: A novel ensemble machine learning-based physical security paradigm using cross-domain ultra-fused feature extraction with hybrid data augmentation scheme

Ahmad Qureshi, Shahzad; Hussain, Lal; Rafique, Muhammad; Sohail, Huniya; Aman, Haroon; Rahat Abbas, Syed; Basit, Muhammad Abdul; Khalid, Muhammad Imran

doi:10.1016/j.eswa.2023.122863

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2024

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Cited by 4 publications

References 61 publications

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Shallow Learning Versus Deep Learning in Speech Recognition Applications

Jiwani,

Gupta

2024

The Springer Series in Applied Machine Learning

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Shallow Learning Versus Deep Learning in Speech Recognition Applications

Jiwani,

Gupta

2024

The Springer Series in Applied Machine Learning

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Enhancing Image Recognition for Security Applications: A Machine Learning Approach

Mahadevan,

Swetha,

Kumudini

et al. 2024

2024 Second International Conference on Advances in Information Technology (ICAIT)

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SFCNN: Separation and Fusion Convolutional Neural Network for Radio Frequency Fingerprint Identification

Wang,

Yao,

Zuo

et al. 2024

International Journal of Intelligent Systems

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The unique fingerprints of radio frequency (RF) devices play a critical role in enhancing wireless security, optimizing spectrum management, and facilitating device authentication through accurate identification. However, high‐accuracy identification models for radio frequency fingerprint (RFF) often come with a significant number of parameters and complexity, making them less practical for real‐world deployment. To address this challenge, our research presents a deep convolutional neural network (CNN)–based architecture known as the separation and fusion convolutional neural network (SFCNN). This architecture focuses on enhancing the identification accuracy of RF devices with limited complexity. The SFCNN incorporates two customizable modules: the separation layer, which is responsible for partitioning the data group size adapted to the channel dimension to keep the low complexity, and the fusion layer which is designed to perform deep channel fusion to enhance feature representation. The proposed SFCNN demonstrates improved accuracy and enhanced robustness with fewer parameters compared to the state‐of‐the‐art techniques, including the baseline CNN, Inception, ResNet, TCN, MSCNN, STFT‐CNN, and the ResNet‐50‐1D. The experimental results based on the public datasets demonstrate an average identification accuracy of 97.78% among 21 USRP transmitters. The number of parameters is reduced by at least 8% compared with all the other models, and the identification accuracy is improved among all the models under any considered scenarios. The trade‐off performance between the complexity and accuracy of the proposed SFCNN suggests that it is an effective architecture with remarkable development potential.

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EML-PSP: A novel ensemble machine learning-based physical security paradigm using cross-domain ultra-fused feature extraction with hybrid data augmentation scheme

Cited by 4 publications

References 61 publications

Shallow Learning Versus Deep Learning in Speech Recognition Applications

Shallow Learning Versus Deep Learning in Speech Recognition Applications

Enhancing Image Recognition for Security Applications: A Machine Learning Approach

SFCNN: Separation and Fusion Convolutional Neural Network for Radio Frequency Fingerprint Identification

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