A Novel Image Encryption Algorithm Based on Cyclic Chaotic Map in Industrial IoT Environments

Abstract

In the Industrial Internet of Things (IIoT), ensuring timely and secure data transmission between sensors and edge devices is paramount, particularly when dealing with sensitive information captured by high-resolution image sensors. However, existing methods often struggle to strike the delicate balance between security and efficiency, resulting in either vulnerable transmissions or significant processing delays. This article proposes a novel image encryption algorithm specifically designed for IIoT environments with constrained resources. The proposed algorithm leverages a new chaotic model that combines a cyclic construction with a 1-D perturbed logistic map. The proposed chaos model produces a single data sequence, subsequently utilized to generate three matrices mirroring the size of the image. Among these matrices, two contribute to crafting a permutation matrix for randomizing pixel positions, while the third matrix facilitates diffusion operations. The chaotic data sequence and generated matrices are preprocessed and can be used for encrypting multiple images under the same session key, enhancing efficiency. Encryption utilizes a single round that combines diffusion and permutation operations simultaneously, further reducing processing time. Experimental results demonstrate its effectiveness on an IoT camera sensor for encryption and a separate device for decryption. Statistical tests confirm the robustness of the encrypted images against various attacks, including correlation analysis, histogram analysis, differential attacks, and key and plaintext sensitivity. Furthermore, comparisons with existing image encryption techniques showcase the proposed algorithm's superior security and efficiency. Notably, it effectively encrypts images of varying sizes, making it suitable for deployment in IIoT environments.

Keywords: Chaotic Image Cipher, Encryption, Industrial Internet Of Things (Iiot), Iot Camera Sensor