Data security in the Industrial Internet of Things (IIoT) through a triple-image encryption framework leveraging 3-D NEAT, 1DCJ, and 4DHCFO techniques

Abstract

In the Industrial Internet of Things (IIoT) era, protecting vast data volumes, including sensitive information, poses a significant security challenge. To address this issue, this research proposes a novel triple-image encryption method tailored for IIoT applications. Unlike conventional algorithms processing a single grayscale image to produce a corresponding single ciphertext, the proposed approach generates a single color encrypted image corresponding to three grayscale input images. This complexity adds an extra layer of challenge for unauthorized individuals attempting to recover plaintext data. Leveraging the 3-D non-equilateral Arnold transform (NEAT), extended one-dimensional chaotic jumping (1DCJ), and a four-dimensional hyperchaotic Chen map of fractional order (4DHCFO), the proposed method begins by processing three grayscale images—Rgray, Ggray, and Bgray—with a 3-D NEAT to scramble their pixel positions. Employing three distinct scrambling operations, multilayer permutation, multiround permutation, and diagonal permutation, enhances scrambling complexity. Subsequently, binary bit planes are extracted and subjected to bit-level scrambling via 1DCJ. Further, a 4DHCFO generates a 16 × 16 substitution box for diffusing scrambled bit planes using XOR operations. Experimental analyses encompassing entropy, correlation, energy, histogram, key sensitivity, key space, NPCR, and UACI reveal the efficacy of the proposed scheme. The scheme demonstrates significant statistical values (entropy: 7.9999, correlation: 0.0001, NPCR: 33.96, UACI: 96.79) and operates efficiently with a computational time of 0.002 for encrypting triple grayscale images simultaneously which shows its suitability for real-time applications.

Keywords Triple grayscale images , Secure data transmission, Bit planes, Internet of things