Saudi Cultural Missions Theses & Dissertations
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Item Restricted CYBERSECURITY OF CRITICAL INFRASTRUCTURE’S MANUFACTURING SYSTEMS A NOVEL FRAMEWORK AND APPROACH FOR PREDICTING CYBERATTACKS BASED ON ATTACKER MOTIVATIONS(Saudi Digital Library, 2025) Alqudhaibi, Adel; Sandeep, JagtapIndustry 4.0 signifies a transformative shift in industrial operations, powered by the integration of automation, connectivity, and digital technologies. This shift enhances diagnostics, autonomous decision-making, automation, and data analysis by machinery and networking equipment, revolutionizing the manufacturing and critical infrastructure sectors. However, the increased reliance on such technologies raises significant cybersecurity concerns. These vulnerabilities are particularly acute in Industrial Control Systems (ICS) , which are commonly used in critical infrastructure (CI) for operational and supervisory control. Industry 4.0 manufacturing systems face increasing cybersecurity threats due to the lack of predictive threat detection, inadequate security frameworks, and growing system complexity. Existing approaches are reactive, failing to incorporate attacker motivations and proactive risk mitigation. As a result, manufacturing systems are exposed to numerous cyber-attacks that can have catastrophic concerns for critical infrastructure sectors such as energy, transportation, and water. Addressing these challenges requires a comprehensive and systematic approach to cybersecurity that is specifically tailored to the nature of these systems. This research introduces a novel cybersecurity approach that predicts potential cyberattacks by considering attacker motivations and the specific characteristics of CI systems. Machine learning (ML) models are employed to predict potential attack methods, offering a proactive solution to prevent cyber threats before they occur. This approach demonstrates a substantial improvement in predictive accuracy, as confirmed by initial evaluation results. Cybersecurity in CI manufacturing systems remains reactive, relying on post-attack mitigation rather than proactive threat prevention. This research addresses the gap by developing a predictive cybersecurity approach Predicting Cyberattacks in Critical Infrastructures (PCCI) which anticipates cyber threats based on attacker motivations and CI system vulnerabilities. Using machine learning (ML) models, this approach enhances attack method prediction, significantly reducing false positives and improving detection accuracy. The proposed framework shifts cybersecurity from a reactive to a proactive stance, contributing to enhanced resilience in Industry 4.0 environments. Initial tests demonstrate notable improvements in prediction accuracy, validating its potential for real-world application. Beyond the implementation of predictive cybersecurity models, this research presents a comprehensive cybersecurity framework that emphasises sustainability within the manufacturing sector. The framework is structured to protect critical resources by ensuring the confidentiality, integrity, and availability of data, while simultaneously enhancing operational resilience. It incorporates proactive strategies for anticipating cyber threats and underscores the importance of comprehensive employee education at all organisational levels. This framework seeks not only to mitigate immediate security risks but also to integrate long-term resilience into cybersecurity strategies, thereby promoting the sustainability of manufacturing operations. A key finding of this research is the significant gap in robust security standards and proactive measures within the manufacturing sector concerning cybersecurity. Despite the growing adoption of Industry 4.0 technologies, many systems remain vulnerable to cyberattacks due to the absence of sufficient security protocols during the early stages of implementation. The absence of standardized guidelines contributes to insufficient employee knowledge and preparedness, leaving them vulnerable to cybersecurity risks. Addressing these gaps is essential for the manufacturing sector to fully capitalize on Industry 4.0 advancements while ensuring the protection of critical systems from emerging cyber threats. The study concludes by recommending a redirection of security resources and procedures to the manufacturing industry. It emphasises the need for increased investment in employee awareness, training programs, and more robust cybersecurity protocols specifically tailored to the needs of industrial systems. By implementing these recommendations, organisations can better mitigate risks, enhance their cybersecurity posture, and ensure the continuity of critical manufacturing and infrastructure operations in the face of progressing cyberattacks.17 0