Physical layer authenticated image encryption for Iot network based on biometric chaotic signature for MPFrFT OFDM system

Authors

Esam A. A. Hagras
Saad Aldosary
Haitham Khaled, Edith Cowan UniversityFollow
Tarek Hassan

Document Type

Journal Article

Publication Title

Sensors

Publisher

MDPI

School

School of Engineering

RAS ID

62003

Funders

King Saud University

Comments

Hagras, E. A. A., Aldosary, S., Khaled, H., & Hassan, T. M. (2023). Physical layer authenticated image encryption for Iot network based on biometric chaotic signature for MPFrFT OFDM system. Sensors, 23(18), article 7843. https://doi.org/10.3390/s23187843

Abstract

In this paper, a new physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transform–Orthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested for secure image transmission over the IoT network. In addition, a new robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map) is designed and analyzed. Also, a new dynamic chaotic biometric signature (DCBS) generator based on combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. The final output of the proposed DCBS generator is used as a dynamic secret key for the MPFrFT OFDM system in which the encryption process is applied in the frequency domain. The proposed DCBS secret key generator generates a very large key space of (Formula presented.). The proposed DCBS secret keys generator can achieve the confidentiality and authentication properties. Statistical analysis, differential analysis and a key sensitivity test are performed to estimate the security strengths of the proposed DCBS-MP-FrFT-OFDM cryptosystem over the IoT network. The experimental results show that the proposed DCBS-MP-FrFT-OFDM cryptosystem is robust against common signal processing attacks and provides a high security level for image encryption application. © 2023 by the authors.

DOI

10.3390/s23187843

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.