## 3 Results
### Cyberattacks in Smart Grids: Challenges and Implications of Cybersecurity
#### 3.2.2 Pairwise Comparison Matrix, Relative Weights of Criteria, and Weighted Sum of Alternatives
Overall, the alternatives with high weighted sums (A1 and A5) are considered critical for enhancing smart-grid cybersecurity, while those with medium- and low-weighted sums still provide value but may have relatively less impact or priority in the evaluated criteria. Enhancing Cybersecurity of Smart Grids: Unveiling the Difference between AI and ML, Exploring their Potential and Addressing Challenges, and Leveraging MCDM-AHP for Optimal AI Selection  
In this section, we analyze artificial intelligence (AI) techniques for cybersecurity options in smart grids to enhance the security and decision-making processes. \begin{table}
\begin{tabular}{l l} \hline \hline
**Alternatives** & **Weighted Sum** \\ \hline
**A1 “Access Control and Authentication”** & 6.250 \\
**A2 “Encryption”** & 3.739 \\
**A3 “Intrusion Detection and Prevention Systems (IDPS)”** & 5.487 \\
**A4 “Firewalls”** & 4.838 \\
**A5 “Security Information and Event Management (SIEM)”** & 5.762 \\
**A6 “Vulnerability Assessment and Penetration Testing”** & 5.256 \\
**A7 “Security Incident Response”** & 5.297 \\
**A8 “Security Monitoring and Logging”** & 4.688 \\
**A9 “Security Awareness and Training”** & 4.706 \\
**A10 “Data Backup and Recovery”** & 4.690 \\
**A11 “Secure Software Development Lifecycle (SSDL)”** & 4.661 \\
**A12 “Incident Response Coordination”** & 4.730 \\
**A13 “Compliance and Regulatory Requirements”** & 4.349 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Weighted sum for each option. Higher weighted sums indicate better overall performance. 3.1 Understanding the Difference between AI and ML: Benefits and Challenges for Cybersecurity in Smart Grids  
Indeed, AI and ML are two related concepts that play significant roles in cybersecurity but have distinct differences [208]. Key differences:  
* **Scope**: AI is a broader field of computer science that encompasses various techniques to develop intelligent machines or systems capable of performing tasks that typically require human intelligence and cognitive functions--such as reasoning, problem solving, decision-making, perception, and natural language processing--while ML is a specific approach within AI that focuses on learning from data. * **Programming vs. Learning**: AI often involves explicitly programming intelligent behavior or rules, whereas ML is a sunset or application of AI that focuses on training models to learn patterns and make predictions based on data without being explicitly programmed. * **Data Dependency**: ML heavily relies on large datasets for training models and improving performance. The quality and quantity of data play a crucial role in ML algorithms' effectiveness. AI, on the other hand, can utilize various data sources but may not be solely dependent on them. * **Human Intervention**: AI may involve human intervention in designing and specifying rules or heuristics for intelligent systems. ML, however, aims to automate the learning process and reduce human intervention by allowing models to learn from data independently. * **Flexibility**: ML algorithms are flexible and can adapt to new data and patterns, enabling them to handle complex and dynamic tasks. AI, as a broader field, encompasses both rule-based systems and learning-based systems, providing a wider range of approaches for different types of problems. Artificial intelligence (AI) can significantly enhance cybersecurity in smart grids by providing advanced capabilities for threat detection, prevention, and response. They can analyze large volumes of data from various sources, such as network traffic, system logs, and sensor data, to detect anomalies, identify potential cyber threats in real-time, and automate incident response processes. AI techniques power intrusion detection and prevention systems (IDPS), predict equipment failures, and optimize maintenance schedules. They can also analyze threat intelligence data, use behavioral analysis, and employ user and entity behavior analytics (UEBA) to identify emerging threats, detect unauthorized access, and mitigate risks.