## 3 Results
### Cyberattacks in Smart Grids: Challenges and Implications of Cybersecurity
#### 3.3.2 Leveraging MCDM-AHP for Selecting the Optimal AI Option for Cybersecurity in Smart Grids
**\"C12\" Explainability and Transparency**: the ability of the AI system to provide explanations and justifications for its decisions and actions, ensuring transparency and accountability in the cybersecurity processes. These criteria provide a comprehensive framework for evaluating cybersecurity options that incorporate AI techniques and methods in the context of smart grids. By considering these criteria, decision-makers can assess and compare different options to make informed choices that align with the specific needs and requirements of their smart-grid cybersecurity initiatives. The pairwise comparison matrix between criteria is presented in the Table 6.  
Based on the calculated relative weights of criteria (Table 7), we can categorize them into three groups:  
* **High Relative Weights**: C1 \"Security Effectiveness\" (26.6%) has the highest relative weight, indicating its significant importance in evaluating cybersecurity options that incorporate artificial intelligence (AI) techniques. This criterion focuses on the accuracy of threat detection, false positive, and false negative rates, which are crucial for effective security measures in smart grids. * **Medium Relative Weights**: C3 \"Integration and Compatibility\" (21.5%) and C4 \"Performance Impact\" (21.5%) have relatively high weights. These criteria emphasize the seamless integration of AI cybersecurity solutions with the existing infrastructure and the impact on system performance, both of which are important considerations in evaluating options. * **Low Relative Weights**: criteria such as C6 \"Manageability and Usability\" (15.8%) (i.e., the ease of managing and using the cybersecurity options within the smart-grid environment, emphasizing user-friendliness and efficient management practices), C5 \"Cost-Effectiveness\" (8.5%) (i.e., considering the balance between cost and the value of security enhancements), C8 \"Resilience and Redundancy\" (8.3%) (i.e., the ability for the cybersecurity options to provide resilience and redundancy, ensuring the continuity and availability of smart-grid operations even in the face of cyber threats or failures), C10 \"Future Readiness\" (7.6%) (i.e., the preparedness of the cybersecurity options to  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Criteria** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** & **C10** & **C11** & **C12** \\ \hline
**C1** & 1 & 3 & 3 & 3 & 5 & 3 &  
adapt and evolve in response to future cybersecurity challenges and advancements in smart-grid technologies), C7 \"Compliance and Regulatory Requirements\" (7.6%) (i.e., the adherence to relevant compliance and regulatory standards and requirements in the deployment of cybersecurity solutions for smart grids), C2 \"Scalability\" (7.5%) (i.e., ability of the cybersecurity options to handle increased demands and expanding smart-grid networks effectively), C9 \"Vendor Support and Collaboration\" (5.9%), C11 \"Network Segmentation\" (5.9%), and C12 \"Explainability and Transparency\" (5.9%) have lower relative weights. While these criteria are still important, they are relatively less influential compared to the higher-weighted criteria. Overall, the results suggest that security effectiveness, integration and compatibility, and performance impact are key considerations when evaluating cybersecurity options in smart grids. However, it is important to consider all the criteria holistically to make informed decisions that align with the specific needs and requirements of the smart-grid cybersecurity initiatives. We find that the consistency ratio (CR) is 0.857, which is less than the threshold value of 0.1. This indicates acceptable consistency in the pairwise comparison matrix.