| dc.description.abstract | The emergence of Smart Grid as a modernization of the traditional power system interweaves critical infrastructure reliability with the complexities of cybersecurity. This doctoral thesis investigates the multi-faceted cyber risks inherent in Smart Grids, the simulation and detection of cyber attacks, and the methodologies for verifying the consequences of such attacks.
This study starts by addressing the key security challenges and threats facing IoTbased smart grids, highlighting specific vulnerabilities in critical smart grid components and systems such as smart meters, substation automation systems, and distributed energy resources. For instance, it identifies vulnerabilities such as weak authentication mechanisms, unencrypted data transmissions, and exploiting software or firmware vulnerabilities that could be manipulated to disrupt power supply or alter energy consumption readings. By demonstrating how these cybersecurity threats can severely undermine the operational integrity and confidentiality of smart grids, this research empowers stakeholders to grasp the practical implications of these vulnerabilities. The proposed security pyramid approach and strategies for robust defence mechanisms provide a roadmap for implementing the findings, making the stakeholders feel capable of safeguarding against these threats.
The thesis further explores the efficacy of simulating cyber attacks on power grids, using digital substations as a specific use case, to evaluate detection mechanisms. By employing various machine learning algorithms, the research highlights the critical role of accurate simulation environments in developing and fine-tuning cyber attack detection models. The findings suggest that logistic regression and support vector machines can significantly enhance the early detection of sophisticated cyber threats, enabling timely responses to prevent potential disruptions.
In addressing the verification of cyber attack consequences, the thesis emphasizes the critical role of comprehensive and dynamic risk assessments tailored to smart grid environments. This research integrates theoretical models and empirical simulations to evaluate the potential impacts of cyber threats, such as service disruptions, unauthorized data access and compromised operational integrity on smart grid operations, among others. Employing advanced frameworks like the MITRE ATT&CK for Industrial Control Systems and the CIA (Confidentiality, Integrity, and Availability) model, the research enhances the precision of threat modelling and evaluates the effectiveness of mitigation strategies. This thorough approach not only deepens the understanding of the diverse consequences of cyber threats but also validates the resilience of mitigation strategies under realistic conditions, providing stakeholders with a robust foundation to trust the research findings.
In conclusion, this research not only enriches the academic understanding of smart grid cybersecurity but also provides practical insights by offering a detailed analysis of attack vectors, detection methodologies, and risk assessment practices. The thesis equips energy sector stakeholders with a strategic framework to navigate the increasingly complex landscape of cyber threats, thereby enhancing the security posture of Smart Grids. | en_US |