Physical Layer Authenticated Image Encryption for IoT Network Based on Biometric Chaotic Signature for MPFrFT OFDM System.

IoT dynamic chaotic biometric signature encryption physical layer security

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
12 Sep 2023
Historique:
received: 07 08 2023
revised: 23 08 2023
accepted: 06 09 2023
medline: 28 9 2023
pubmed: 28 9 2023
entrez: 28 9 2023
Statut: epublish

Résumé

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 22200. 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.

Identifiants

pubmed: 37765900
pii: s23187843
doi: 10.3390/s23187843
pmc: PMC10536343
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : King Saud University
ID : RSP2023R260

Références

Sensors (Basel). 2023 Jul 21;23(14):
pubmed: 37514882
Sensors (Basel). 2023 Feb 06;23(4):
pubmed: 36850412
Entropy (Basel). 2018 Sep 23;20(10):
pubmed: 33265819
IEEE Trans Cybern. 2015 Sep;45(9):2001-12
pubmed: 25373135
Comput Methods Programs Biomed. 2023 Nov;241:107745
pubmed: 37579550

Auteurs

Esam A A Hagras (EAA)

Faculty of Engineering, Delta University for Science and Technology, Gamasa 35712, Egypt.

Saad Aldosary (S)

Department of Computer Science, Community College, King Saud University, Riyadh 11437, Saudi Arabia.

Haitham Khaled (H)

Department of Electronics and Communications, School of Engineering, Edith Cowan University, Perth, WA 6027, Australia.

Tarek M Hassan (TM)

Faculty of Engineering, Delta University for Science and Technology, Gamasa 35712, Egypt.

Classifications MeSH