RNA-TransCrypt: Image Encryption Using Chaotic RNA Encoding, Novel Transformative Substitution, and Tailored Cryptographic Operations

• URL: http://arxiv.org/abs/2401.04707v1
• Date: Tue, 9 Jan 2024 18:11:12 GMT
• Title: RNA-TransCrypt: Image Encryption Using Chaotic RNA Encoding, Novel Transformative Substitution, and Tailored Cryptographic Operations
• Authors: Muhammad Shahbaz Khan, Jawad Ahmad, Ahmed Al-Dubai, Baraq Ghaleb, Nikolaos Pitropakis, William J. Buchanan,
• Abstract summary: RNA-TransCrypt is a novel image encryption scheme that is highly secure but also efficient and lightweight. RNA-TransCrypt integrates the biocryptographic properties of RNA encoding with the non-linearity and unpredictability of chaos theory.
• Score: 2.2351927942921366
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Given the security concerns of Internet of Things (IoT) networks and limited computational resources of IoT devices, this paper presents RNA-TransCrypt, a novel image encryption scheme that is not only highly secure but also efficient and lightweight. RNA-TransCrypt integrates the biocryptographic properties of RNA encoding with the non-linearity and unpredictability of chaos theory. This scheme introduces three novel contributions: 1) the two-base RNA encoding method, which transforms the image into RNA strands-like sequence, ensuring efficient scrambling; 2) the transformative substitution technique, which transforms the s-box values before replacing the pixel values, and is responsible for making the scheme lightweight; and 3) three mathematical cryptographic operations designed especially for image encryption that ensure the effective transformation of the s-box values, resulting in a new outcome even for the same input values. These modules are key-dependent, utilizing chaotic keys generated by the De Jong Fractal Map and the Van der Pol Oscillator. Extensive security analysis, including histogram analysis, correlation analysis, and the results of the statistical security parameters obtained from the Gray-Level Co-occurrence Matrix (GLCM) validate the efficacy of the proposed scheme in encrypting input images with close-to-ideal results of 7.997 entropy and 0.0006 correlation.

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