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

TL;DR

RNA-TransCrypt is a lightweight, secure image encryption scheme for IoT devices that combines RNA encoding, chaos theory, and novel cryptographic operations to achieve high security and efficiency.

Contribution

It introduces a new RNA encoding method, a transformative substitution technique, and chaos-based cryptographic operations tailored for image encryption.

Findings

Achieved high entropy of 7.997 indicating strong randomness.

Obtained near-zero correlation of 0.0006 demonstrating effective encryption.

Validated security through histogram, correlation, and GLCM analysis.

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.