Author(s)

Nicolas Ferry¹, Paul-Eric Dossou^2,3, Gabriel Ihowa², Gaspard Laouenan²

¹Icam Nantes, Carquefou, France.
²Icam Grand Paris Sud, Carré Sénart, Lieusaint, France.
³IFSTTAR/AME/SPLOTT, Université Gustave Eiffel, Marne-la-Vallée, France.

Companies are generally focused on how to improve their global performance. Concepts, methods and tools are regularly used to transform them. Key performance indicators are used to measure how performance is increased. Industry 4.0 concepts and sustainability expectations actually contribute to this performance improvement. Indeed, cybersecurity as one of these concepts is required to increase the company performance. Even if it is well-known and applied in companies through the protection of their information systems, progress is expected in research on how to ensure the security of data and factory processes in the manufacturing, as the number of cyberattacks towards industries is growing these last few years. This paper aims to increase the company performance and sustainability to enforce factory machines protection by creating private security network groups. But currently, most of the Programmable Logic Controller PLC protocols have not been securely designed. Thus, the creation of secure groups of machines by combining strong authentication, strong or lightweight ciphering, and data stream integrity is proposed. The security is enforced by a continuous key’s renewal algorithm. An experiment on an industry’s architecture has been led to validate the concepts of the proposition. The study is compared to existing OPC-UA and MACsec standards in terms of drawbacks and advantages. This work could be implemented in hardware for further performance improvement.

Sustainable Industries, Cybersecurity, Networks, Protocols, Side-Channel Analysis

Ferry, N. , Dossou, P. , Ihowa, G. and Laouenan, G. (2023) Enhancing Security for Legacy Factory Machines: A Continuous Key Renewal Algorithm for Securing Group of Machines. Journal of Software Engineering and Applications, 16, 714-743. doi: 10.4236/jsea.2023.1612037.