CellSecure: Securing Image Data in Industrial Internet-of-Things via Cellular Automata and Chaos-Based Encryption

被引:1
|
作者
Ali, Hassan [1 ]
Khan, Muhammad Shahbaz [2 ]
Driss, Maha [3 ,4 ]
Ahmad, Jawad [2 ]
Buchanan, William J. [2 ]
Pitropakis, Nikolaos [2 ]
机构
[1] HITEC Univ Taxila, Dept Elect Engn, Taxila, Pakistan
[2] Edinburgh Napier Univ, Sch Comp Engn & Built Environm, Edinburgh, Midlothian, Scotland
[3] Prince Sultan Univ, Robot & Internet Things Lab, Riyadh, Saudi Arabia
[4] Univ Manouba, Natl Sch Comp Sci, RIADI Lab, Manouba, Tunisia
关键词
cellular automata; chaos; image encryption; industrial internet of things; industry; 4.0; IIoT;
D O I
10.1109/VTC2023-Fall60731.2023.10333478
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
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页数:6
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