In the context of the widespread application of robotics technology across numerous industrial sectors, the security of data communication in industrial robot arms emerges as a paramount concern. These robotic arms are instrumental in enhancing productivity and safety in a variety of fields, including but not limited to transportation, agriculture, construction, and mining, by automating tasks and reducing human exposure to hazardous conditions. This paper proposes a novel hybrid encryption strategy to fortify the data security of these industrial robot arms, particularly focusing on preventing data breaches during both wired and wireless communications. The suggested encryption framework combines the strengths of Elliptic Curve Cryptography (ECC) for its efficient asymmetric encryption capabilities, ChaCha20 for its rapid and low-energy symmetric encryption, and Poly1305 for ensuring data integrity through its message authentication code (MAC) algorithm. By leveraging these technologies, the paper outlines the development and application of a secure communication protocol, implemented using Python, that guarantees the confidentiality and integrity of data shared among robot arms and between these arms and their control systems. Additionally, the research conducts a comparative analysis between the ECC-based method and the RSA encryption standard, highlighting the efficiency and effectiveness of the proposed hybrid approach through various tests on different data types and sizes. The findings illustrate a marked improvement in safeguarding against potential data leaks, thereby significantly contributing to the enhancement of industrial robot arms' data security. This study not only addresses the pressing need for robust data protection mechanisms in the face of evolving cyber threats but also sets a benchmark for future research in the field of industrial robotics security.
Primary Language | English |
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Subjects | Cybersecurity and Privacy (Other), Mechanical Engineering (Other) |
Journal Section | Research Articles |
Authors | |
Early Pub Date | August 1, 2024 |
Publication Date | August 31, 2024 |
Submission Date | February 21, 2024 |
Acceptance Date | June 14, 2024 |
Published in Issue | Year 2024 Volume: 28 Issue: 4 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.