## 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  
In this section, we delve into the evaluation of cybersecurity options using AI in the context of smart grids. We begin by establishing a set of criteria to assess the effectiveness of these options for ensuring robust cybersecurity. We then employ a pairwise comparison matrix (Table 6) to determine the relative importance of each criterion when compared to others (Table 7). These relative weights are used to calculate the weighted sum for each cybersecurity option (Table 8), yielding a comprehensive score that takes into account the significance of each criterion. Through this evaluation process, we gain valuable insights into the strengths and weaknesses of different cybersecurity options considering AI in addressing the specific needs of smart grids. In the field of cybersecurity for smart grids, a variety of AI techniques and methods can be applied to enhance security measures [211]. Here are several types and methods of AI commonly used in cybersecurity for smart grids:  
1. **\"A1\" Machine Learning (ML)**:  
**Supervised Learning**: ML models are trained on labeled data to make predictions or classifications based on known patterns. * **Unsupervised Learning**: ML models analyze unlabeled data to identify patterns, anomalies, or clustering structures without prior knowledge. * **Semi-Supervised Learning**: a combination of labeled and unlabeled data is used to train ML models, making use of both supervised and unsupervised techniques. * **Reinforcement Learning**: ML models learn to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. 2. **\"A2\" Deep Learning (DL)**:
* **Convolutional Neural Networks (CNNs)**: DL models designed for processing structured grid-like data, such as image or time-series data. * **Recurrent Neural Networks (RNNs)**: DL models that handle sequential or time-series data by maintaining internal memory. 3. **\"A3\" Natural Language Processing (NLP)**:
* **Sentiment Analysis**: NLP techniques are used to analyze text data and determine the sentiment or opinion expressed. * **Named Entity Recognition**: NLP models identify and classify named entities, such as person names, locations, or organizations, in text data. * **Text Classification**: NLP algorithms classify text documents into predefined categories based on their content. 4. **\"A4\" Genetic Algorithms (GA)**: GA methods can be employed to optimize complex problems, such as finding optimal security configurations or identifying optimal control parameters. 5. **\"A5\" Fuzzy Logic**: fuzzy logic models capture and reason with imprecise or uncertain data, enabling decision-making in scenarios with ambiguity or vagueness. 6. **\"A6\" Expert Systems**:
* **Rule-Based Systems**: expert systems use predefined rules and knowledge bases to mimic the decision-making processes of human experts. * **Knowledge Representation**: techniques for capturing and representing domain-specific knowledge to support decision-making and inference. 7. **\"A7\" Swarm Intelligence**:
* **Particle Swarm Optimization (PSO)**: swarm intelligence methods are used for optimization problems where a group of particles iteratively searches for the optimal solution. * **Ant Colony Optimization (ACO)**: swarm intelligence techniques inspired by the foraging behavior of ants to solve complex optimization problems. 8. **\"A8\" Bayesian Networks**: probabilistic models that represent uncertain relationships between variables using directed acyclic graphs are useful for modeling and reasoning under uncertainty. 9. **\"A9\" Hybrid Approaches**: combinations of different AI techniques, such as integrating ML algorithms with expert systems or using hybrid models that combine DL and traditional ML methods. These are some of the prominent types and methods of AI used in cybersecurity for smart grids.