In the era of Industrial IoT (IIoT) and Industry 4.0, ensuring secure data transmission has become a critical concern. Among other data types, images are widely transmitted and utilized across various IIoT applications, ranging from sensor-generated visual data and real-time remote monitoring to quality control in production lines. The encryption of these images is essential for maintaining operational integrity, data confidentiality, and seamless integration with analytics platforms. This paper addresses these critical concerns by proposing a robust image encryption algorithm tailored for IIoT and Cyber-Physical Systems (CPS). The algorithm combines Rule-30 cellular automata with chaotic scrambling and substitution. The Rule 30 cellular automata serves as an efficient mechanism for generating pseudo-random sequences that enable fast encryption and decryption cycles suitable for real-time sensor data in industrial settings. Most importantly, it induces non-linearity in the encryption algorithm. Furthermore, to increase the chaotic range and keyspace of the algorithm, which is vital for security in distributed industrial networks, a hybrid chaotic map, i.e., logistic-sine map is utilized. Extensive security analysis has been carried out to validate the efficacy of the proposed algorithm. Results indicate that our algorithm achieves close-to-ideal values, with an entropy of 7.99 and a correlation of 0.002. This enhances the algorithm's resilience against potential cyber-attacks in the industrial domain.

翻译：在工业物联网（IIoT）与工业4.0时代，确保数据传输的安全性已成为关键挑战。在各类数据类型中，图像被广泛应用于传感器生成视觉数据、实时远程监控及生产线质量控制等IIoT场景。对这些图像进行加密，对维护系统运行完整性、数据保密性及与分析平台的无缝集成至关重要。本文针对这些关键问题，提出了一种专为IIoT和网络物理系统（CPS）设计的鲁棒图像加密算法。该算法将Rule-30元胞自动机与混沌置乱和替换相结合：Rule-30元胞自动机作为生成伪随机序列的高效机制，可实现适用于工业场景实时传感器数据的快速加解密周期，更重要的是，它能在加密算法中引入非线性特性。此外，为扩展分布式工业网络中算法至关重要的混沌范围与密钥空间，采用了一种混合混沌映射（即逻辑-正弦映射）。通过全面的安全性分析验证了所提算法的有效性。结果表明，本算法实现了接近理想值的性能指标，信息熵达7.99，相关系数仅为0.002，显著增强了算法在工业领域抵御潜在网络攻击的鲁棒性。