Data Trustworthiness in Critical Infrastructures Protection

Critical Infrastructures (CIs) form the backbone of modern societies, ensuring the delivery of vital goods and services, and their disruption can have profound implications for both safety and security. Moreover, the emergence of Smart Infrastructures and the Internet of Things (IoT) has underscored the critical role of data in CIs, bringing new challenges for cybersecurity. This dissertation explores security challenges of CIs, especially against cyber-attacks targeting data, presenting solutions for security monitoring and data protection in CIs. The key contributions of this work include a literature review on Data Provenance in CIs, the development and validation of an Advanced Tamper-Resistant Storage (ATRS) framework, a Cyber-Attack Detection Framework (CADF), and the integration of both. Machine Learning (ML) techniques are employed to enhance CADF's accuracy in detecting attacks, featuring Association Rule Mining (ARM) and Explainable Artificial Intelligence (xAI), showing promising experimental results. This research seeks to enhance the resilience of critical infrastructures by providing a comprehensive solution and methodology for preventing and detecting data-centric cyber-attacks. By securing data and improving security monitoring, it offers a robust defense against threats that could otherwise have catastrophic consequences.