# Cyber Insurance for Energy Economic Risks
## 6 Comparative Analysis of Cybersecurity Methodologies  
This study integrates and applies the methodologies from two recent research papers, adapting their innovative approaches to unique scenarios for a comprehensive comparative analysis [30, 32]. Initially, the coalitional cyber insurance model presented in the first referenced paper was applied to a collaborative framework among multiple energy system operators within a simulated smart city environment [32]. This model enables the formation of a risk-sharing coalition, where premiums are calculated based on shared data regarding vulnerabilities and historical cyber incident data, thereby attempting to mitigate the financial impact across all participating entities. Additionally, the actuarial framework, using semi-Markov processes, is incorporated, modeling the durations and state transitions of the cyberattacks that are affecting the smart city's energy infrastructure [30]. This adaptation involves developing a stochastic model to assess the interdependencies among cyber risks across the integrated energy systems, employing Monte Carlo simulations to predict the impact and effectiveness of the proposed insurance schemes under various cyberattack scenarios. Through these applications, the proposed approach not only leverages the existing theoretical advancements but also enhances them by tailoring their implementation to fit the specific context and challenges of the scenarios. The outcomes of applying these methods are then meticulously compared with the results derived from the proposed proprietary method, focusing on several key performance indicators, such as risk mitigation efficiency, cost reduction, operational resilience, and recovery capabilities. This comparative analysis seeks to underline the advantages and potential limitations of each approach, providing a nuanced understanding of their applicability and effectiveness in real-world settings. By methodically evaluating these adapted methodologies against the proposed approach, the study aims to demonstrate the superiority, or in certain cases, the complementary nature of the proposed method, offering valuable insights into the strategic management of cyber risks in critical infrastructure environments. In Table 4, a comparative analysis of performance metrics across three different methodologies applied to cyber risk management in power systems is presented. The table focuses on two key performance indicators: cost reduction and risk mitigation efficiency. Cost  
Figure 8: Load shedding comparison under different scenarios. reduction (%) is calculated as the percentage decrease in the total financial expenditure associated with managing and mitigating cyber risks compared to a baseline scenario without the implemented method. Risk mitigation efficiency (%) measures the effectiveness of a method in reducing the frequency and severity of cyber incidents, thereby maintaining system stability and operational continuity. The proposed DRO method demonstrates superior performance in both metrics compared to the other two methodologies. It achieves a cost reduction of 12.5%, which is 2.3% higher than that achieved by the Coalitional Cyber Insurance Model and 3.8% higher than the Actuarial Cybersecurity Framework. This indicates a more efficient allocation and utilization of resources under the DRO method, potentially leading to significant savings in operational costs for power systems. In terms of risk mitigation efficiency, the DRO method also leads with an 82.3% efficiency rate, surpassing the Coalitional Cyber Insurance Model by 6.8% and surpassing the Actuarial Cybersecurity Framework by 11.5%. This enhanced efficiency underscores the effectiveness of the DRO method in not only identifying and responding to cyber threats but also in maintaining system stability and reducing potential disruptions caused by such threats. The results underscore the DRO method's robustness and adaptability in handling the uncertainties and dynamics of cyber threats more effectively than the other tested methods.