Document Type
Journal Article
Publication Title
Sensors
Volume
23
Issue
14
PubMed ID
37514882
Publisher
MDPI
School
School of Engineering
RAS ID
61857
Funders
King Saud University
Abstract
The demand for cybersecurity is growing to safeguard information flow and enhance data privacy. This essay suggests a novel authenticated public key elliptic curve based on a deep convolutional neural network (APK-EC-DCNN) for cybersecurity image encryption application. The public key elliptic curve discrete logarithmic problem (EC-DLP) is used for elliptic curve Diffie–Hellman key exchange (EC-DHKE) in order to generate a shared session key, which is used as the chaotic system’s beginning conditions and control parameters. In addition, the authenticity and confidentiality can be archived based on ECC to share the (Formula presented.) parameters between two parties by using the EC-DHKE algorithm. Moreover, the 3D Quantum Chaotic Logistic Map (3D QCLM) has an extremely chaotic behavior of the bifurcation diagram and high Lyapunov exponent, which can be used in high-level security. In addition, in order to achieve the authentication property, the secure hash function uses the output sequence of the DCNN and the output sequence of the 3D QCLM in the proposed authenticated expansion diffusion matrix (AEDM). Finally, partial frequency domain encryption (PFDE) technique is achieved by using the discrete wavelet transform in order to satisfy the robustness and fast encryption process. Simulation results and security analysis demonstrate that the proposed encryption algorithm achieved the performance of the state-of-the-art techniques in terms of quality, security, and robustness against noise- and signal-processing attacks.
DOI
10.3390/s23146589
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Hagras, E. A. A., Aldosary, S., Khaled, H., & Hassan, T. M. (2023). Authenticated public key elliptic curve based on deep convolutional neural network for cybersecurity image encryption application. Sensors, 23(14), article 6589. https://doi.org/10.3390/s23146589