ȘI MACHINE LEARNING ÎN PROTECȚIA INFRASTRUCTURII CRITICE: IMPLICAȚII PENTRU ORDINEA PUBLICĂ ÎN INDUSTRY 5.0
BLOCKCHAIN AND MACHINE LEARNING FOR CRITICAL INFRASTRUCTURE PROTECTION: IMPLICATIONS FOR PUBLIC ORDER IN INDUSTRY 5.0

Summary/Abstract: This article examines the synergy between blockchain and machine learning (ML) technologies for securing critical infrastructure, with a focus on cyber risks in the transition to the Industry 5.0 era. Drawing on information security theory and case studies such as the TradeLens platform developed by Maersk and IBM, as well as Renault's XCEED solution, the paper explores how blockchain ensures immutable traceability and decentralization, while ML enables predictive threat detection through real-time data analysis. In the Romanian context, it highlights vulnerabilities in the Dacia-Renault automotive cluster, impacted by major cyber attacks like WannaCry in 2017, and proposes integrated strategies for public order forces. These include leveraging these technologies to prevent attacks on energy networks, transportation, and financial systems, aligned with ESG (Environmental, Social, Governance) objectives and national strategic resilience. The research emphasizes the need for a human-centric approach in Industry 5.0, where human-machine collaboration enhances security but introduces new risks such as scalability and data privacy. By integrating these technologies, cyber incidents can be reduced by up to 40%, according to recent studies, contributing to public order maintenance through advanced technological intelligence. The paper proposes a conceptual framework for implementation in Romania, including training for police officers and public-private partnerships.

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