The developed system optimizes the authentication and the encryption communication of remote maintenance access. In addition, it improves the traceability management, which helps to ameliorate ground needs related to error management, as well as to decrease legal risk by tooling up legal management for all the involved economic actors. Moreover, work on OT domain has been led with behavioral detection (machine learning algorithm for example) based on OT network traffic of IDS sensor and OT SIEM with special HMI’s for the factory. This work describes a methodology that provides insights and proposes architecture for a behavior-based anomaly detection agent that can detect previously unknown anomalies while adapting to the current operational context. In addition to the behavioral detection, the attack detection was completed with OT forensic study. The solution has been shaped to favor a greater protection of industrial secret as well as a higher availability rate of the production line.
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Final conclusions are still to be finalized as the project is running until May 2022. However, we know that the demonstration enriches SeCoIIA scenarios applied to the Aerospace, Automotive, Naval and Robotic manufacturing industries. Future partners may be interested to join for new EU R&I projects as we have deep knowledge of industrial use-cases related to Smart industry and cybersecurity. More precisely, based on the SECOIIA results, we will be able to bring strong expertise in cybersecurity performance monitoring, including on the supply chain, for the factories of the future.
- Research and Technology Organisations
- Academia/ Universities
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The secure remote maintenance of maritime construction system has confirmed our hypothesis and are providing efficient results. Higher TRL may be considered at medium term.
The results obtained during the project will support the deployment of the secure system. Research activities will be continued to favor the industrialization process.
The results will favor the replication of the secure system. The results may apply to manufacturing other sectoral industries as the other solutions explored within the SECOIIA project.
Being strongly innovative, the solution will have a long-term life, enabled by future integrations.
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