## 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
Based on the calculated weighted sums (Table 8), which represent the overall evaluation of each AI technique or method in addressing the cybersecurity needs of smart grids, considering the given criteria and their relative weights, we can categorize the alternatives into three groups:  
* **High Weighted Sum**: \"Deep Learning (A2)\" emerges as the alternative with the highest weighted sum (0.635), indicating its strong potential in enhancing cybersecurity in smart grids. It demonstrates favorable performance across multiple criteria and aligns well with the specific requirements of the evaluated criteria. * \"Hybrid approaches (A9)\" show a relatively medium weighted sum (0.574), suggesting their effectiveness in addressing cybersecurity challenges in smart grids. These approaches combine multiple AI techniques and methods to leverage their respective strengths and overcome limitations. * **\"Bayesian Networks (A8)\" (0.531) also fall into the medium-weighted sum category, indicating their relevance and potential in addressing cybersecurity needs. They use probabilistic modeling to capture dependencies and uncertainties in smart-grid systems. * \"Swarm Intelligence\" (_A7_): This alternative also falls within the medium range of weighted sums (0.405). Swarm intelligence techniques, such as particle swarm optimization and ant colony optimization, offer potential benefits for optimizing cybersecurity solutions in smart grids. \begin{table}
\begin{tabular}{l c} \hline \hline
**Criteria** & **Relative Weights (RWs) of Criteria** \\ \hline C1 \"Security Effectiveness\" & 26.6\% \\ C2 \"Scalability\" & 7.5\% \\ C3 \"Integration and Compatibility\" & 21.5\% \\ C4 \"Performance Impact\" & 21.5\% \\ C5 \"Cost-Effectiveness\" & 8.5\% \\ C6 \"Manageability and Usability\" & 15.8\% \\ C7 \"Compliance and Regulatory Requirements\" & 7.6\% \\ C8 \"Resilience and Redundancy\" & 8.3\% \\ C9 \"Vendor Support and Collaboration\" & 5.9\% \\ C10 \"Future Readiness\" & 7.6\% \\ C11 \"Network Segmentation\" & 5.9\% \\ C12 \"Explainability and Transparency\" & 5.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Relative weights of the criteria. \"Machine Learning (ML) (A1)\": This alternative has a moderate weighted sum (0.453), indicating that machine learning techniques have some value but are not ranked as highly as deep learning or hybrid approaches. * **Low Weighted Sum**:
* \"Fuzzy Logic (A5)\": This alternative has a relatively lower weighted sum (0.324), suggesting that fuzzy logic methods may have limited applicability or effectiveness compared to other alternatives. * \"Natural Language Processing (NLP) (A3)\": this alternative also falls within the lower range of weighted sums (0.312). While NLP techniques can be useful for certain cybersecurity tasks, they may not be as prominent or effective as other alternatives in the context of smart-grid security. * \"Expert Systems (A6)\": this alternative has a lower weighted sum (0.247), indicating that expert systems may have limitations or are perceived to have less impact on smart-grid cybersecurity compared to other approaches. * \"Genetic Algorithms (GA) (A4)\": This alternative has the lowest weighted sum (0.148) among the options, suggesting that genetic algorithms may have limited applicability or effectiveness for addressing smart-grid cybersecurity challenges. Discussion: Advantages, Limitations, Enhancements of MCDM-AHP in Evaluating Cybersecurity Options in Smart Grids: Exploring Alternative MCDM Approaches  
This study employs the multi-criteria decision-making analytical hierarchy process (MCDM-AHP) methodology to evaluate and determine the most effective cybersecurity options for smart grids. While the AHP approach offers various advantages (Section 4.1), it also presents several challenges (Section 4.2) and improvements (Section 4.3) that need to be addressed in the evaluation process [212].