## 3 Results
### Enhancing MCDM-AHP for Smart-Grid Cybersecurity: Exploring Strategies for Improvement  
Improving the multi-criteria decision making with the analytical hierarchy process (MCDM-AHP) methodology for smart-grid cybersecurity involves enhancing its effectiveness, adaptability, and robustness in addressing the complex and evolving challenges posed by cyber threats. Below are several approaches to enhancing MCDM-AHP for smart-grid cybersecurity:  
* **Incorporate More Comprehensive Criteria**: expand the list of evaluation criteria to encompass a broader range of factors relevant to smart-grid cybersecurity. Consider including technical, economic, social, environmental, and regulatory criteria to capture a holistic view of the cybersecurity landscape.
* **Quantify Qualitative Criteria**: develop methods to quantify qualitative criteria, such as expert opinions, using appropriate scales or weights. This allows for a more objective assessment and comparison of cybersecurity measures.
* **Dynamic Weight Adjustments**: implement dynamic weight adjustments to reflect the changing significance of criteria over time or in response to evolving cyber threats. This ensures that the decision-making process remains adaptable and aligned with the emerging challenges.
* **Consider Uncertainty and Risk**: integrate methods to handle uncertainty and risk, such as probabilistic modeling and sensitivity analysis. This enhances the robustness of the decision-making by accounting for the inherent uncertainties in the cybersecurity scenarios.
* **Integrate Real-Time Data**: incorporate real-time data feeds and analytics into the MCDM-AHP framework to enable continuous monitoring and adjustment of the cybersecurity measures based on the current threat landscape.
* **Include Machine Learning**: integrate machine learning algorithms to enhance predictive capabilities and automate the identification of potential threats and vulnerabilities. ML can assist in identifying patterns and anomalies that may not be evident through traditional approaches.
* **Scenario Analysis**: conduct a comprehensive scenario analysis to evaluate the performance of the cybersecurity measures under various attack scenarios. This helps identify potential weaknesses and prioritize measures that demonstrate robustness across different threats.
* **Stakeholder Engagement**: involve a diverse group of stakeholders, including cybersecurity experts, grid operators, regulators, and end-users, in the decision-making process. Their insights can contribute to a more holistic and informed assessment.
* **Feedback Mechanisms**: implement feedback mechanisms that allow for continuous learning and improvement of the MCDM-AHP model based on the outcomes of previous decisions and their real-world impacts.
* **Interdisciplinary Collaboration**: foster collaboration between cybersecurity experts, power engineers, data scientists, and decision analysts to ensure a well-rounded and multidisciplinary approach to improving MCDM-AHP for smart-grid cybersecurity.  
By implementing these strategies, the MCDM-AHP methodology can be enhanced to provide more accurate, adaptable, and effective decision support for selecting optimal cybersecurity measures in smart grids.