## 5 Conclusions
### General Findings and Discussion  
Based on the relative weights of the criteria, we can categorize them into three groups: criteria with high relative weights (above 10%), including \"security effectiveness\" with the highest weight, indicating its significant importance; criteria with medium relative weights (between 7% and 10%), such as \"cost-effectiveness\", \"scalability\", and \"integration and compatibility\", which are moderately important; and criteria with low relative weights (below 7%), including \"performance impact\" and several others (i.e., manageability and usability, compliance and regulatory requirements, resilience and redundancy, vendor support and collaboration, future readiness), which contribute to the overall evaluation but have relatively less influence. While the impact of the lower-weighted criteria is smaller, they should still be considered alongside other factors. Based on the weighted sums of the alternatives, we can categorize them into three groups: alternatives with high weighted sums (above 5), including \"access control and authentication\" and \"Security Information and Event Management (SIEM)\", which are crucial for enhancing smart-grid cybersecurity; alternatives with medium weighted sums (between 5 and 5.5), such as \"Intrusion Detection and Prevention Systems (IDPS)\" and \"security incident response\", which offer solid performance in detecting and responding to security incidents; and alternatives with low weighted sums (below 4.5), such as \"compliance and regulatory requirements\" and \"encryption\", which provide value in their respective criteria but may not excel as strongly overall. Based on the relative weights of the criteria, we can categorize them into three groups for evaluating cybersecurity options that incorporate artificial intelligence (AI) techniques in smart grids. The criteria with high relative weights, such as \"security effectiveness\", emphasize the accuracy of threat detection and the importance of AI in effective security measures. The criteria with medium relative weights, including \"integration and compatibility\" and \"performance impact\", highlight the need for seamless integration and the impact on system performance. The criteria with low relative weights, such as \"manageability and usability\", \"cost-effectiveness\", \"resilience and redundancy\", \"future readiness\", \"compliance and regulatory requirements\", \"scalability\", \"vendor support and collaboration\", \"network segmentation\", and \"explainability and transparency\", contribute to the evaluation but have less influence compared to higher-weighted criteria. Overall, security effectiveness, integration and compatibility, and performance impact are key considerations when evaluating AI-based cybersecurity options for smart grids, while also taking into account the other criteria to make well-rounded decisions aligned with smart-grid cybersecurity needs. Based on the weighted sums, the alternatives for addressing the cybersecurity needs of smart grids can be categorized into three groups. The alternatives with high weighted sums, such as \"deep learning\", demonstrate strong potential and favorable performance across multiple criteria. The alternatives with medium weighted sums, including \"hybrid approaches\", \"Bayesian networks\", \"swarm intelligence\", and \"machine learning\", offer effectiveness and relevance in addressing cybersecurity challenges. The alternatives with low weighted sums, such as \"fuzzy logic\", \"natural language processing (NLP)\", \"expertsystems\", and \"genetic algorithms (GA)\", may have limited applicability or effectiveness compared to other options. These findings provide insights into the effectiveness and suitability of AI techniques and methods for enhancing smart-grid cybersecurity. The advantages and limitations of MCDM-AHP are addressed, highlighting its potential shortcomings in capturing the full complexity of cybersecurity decision-making and the subjective nature of assigning weights to criteria.