Drone-Captured Wildlife Data Encryption: A Hybrid 1D–2D Memory Cellular Automata Scheme with Chaotic Mapping and SHA-256

Akram Belazi and Héctor Migallón ()
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Akram Belazi: Laboratory RISC-ENIT (LR-16-ES07), Tunis El Manar University, Tunis 1002, Tunisia
Héctor Migallón: Department of Computer Engineering, Miguel Hernández University, 03202 Elche, Spain

Mathematics, 2024, vol. 12, issue 22, 1-27

Abstract: In contemporary wildlife conservation, drones have become essential for the non-invasive monitoring of animal populations and habitats. However, the sensitive data captured by drones, including images and videos, require robust encryption to prevent unauthorized access and exploitation. This paper presents a novel encryption algorithm designed specifically for safeguarding wildlife data. The proposed approach integrates one-dimensional and two-dimensional memory cellular automata (1D MCA and 2D MCA) with a bitwise XOR operation as an intermediate confusion layer. The 2D MCA, guided by chaotic rules from the sine-exponential (SE) map, utilizes varying neighbor configurations to enhance both diffusion and confusion, making the encryption more resilient to attacks. A final layer of 1D MCA, controlled by pseudo-random number generators, ensures comprehensive diffusion and confusion across the image. The SHA-256 hash of the input image is used to derive encryption parameters, providing resistance against plaintext attacks. Extensive performance evaluations demonstrate the effectiveness of the proposed scheme, which balances security and complexity while outperforming existing algorithms.

Keywords: wildlife conservation; drone data encryption; chaotic systems; memory cellular automata; diffusion; confusion; image security (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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