### Knowledge Gaps  
The existing research has identified several knowledge gaps in the fields of cyberattacks and cybersecurity in smart grids.  
First, there is a need for a comprehensive overview of smart-grid components and applications, including the identification of vulnerable components and potential types of cyberattacks. Furthermore, understanding the short-term and long-term techno-economic-safety-social impacts of cyberattacks on smart grids, including the cascading effects on interconnected components, is crucial. The real-world case studies of cyberattacks on operational smart grids and the development of quantitative models and metrics to assess the impacts of cyberattacks are necessary. Additionally, the implications of cybersecurity measures on operational smart grids and the development of quantitative models and metrics to assess the implications of cybersecurity on smart grids need further exploration.  
Second, research on the use of the analytical hierarchy process (AHP) to solve multi-criteria decision-making (MCDM) problems in the context of cybersecurity in smart grids is relatively limited, as AHP is not commonly associated with cybersecurity specifically. However, AHP is a widely used decision-making tool that can be applied to various domains--such as renewable energy integration [88], the performance of residential energy management control algorithms [89], a day-ahead scheduling of electric vehicles and electrical storage systems in smart homes [90], optimization of the advanced metering infrastructure (AMI) customer ecosystem [91], including cybersecurity [92, 93]. Therefore, the optimal cybersecurity options for smart grids, such as access control, authentication, encryption, intrusion detection systems (IDS), firewalls, security information and event management (SIEM), and others, need to be evaluated based on multiple criteria. These criteria include security effectiveness, scalability, integration, and compatibility; performance impact; cost-effectiveness; manageability and usability; compliance and regulatory requirements; resilience and redundancy; vendor support and collaboration; future readiness; network segmentation; patch management; threat intelligence; and vendor and supply chain security.  
Third, understanding the potential and challenges of AI techniques, such as machine learning, deep learning, natural language processing (NLP), genetic algorithms (GA), fuzzy logic, expert systems, swarm intelligence, Bayesian networks, and hybrid approaches, is important. The use of MCDM-AHP to find the optimal cybersecurity option that in corporates AI techniques and methods needs to be explored. The criteria for evaluation may include security effectiveness, scalability, integration, and compatibility; performance impact; cost-effectiveness; manageability and usability; compliance and regulatory requirements; resilience and redundancy; vendor support and collaboration; future readiness; network segmentation; and explainability and transparency.  
The advantages and drawbacks of employing AHP to assess cybersecurity options in smart grids need to be discussed. Furthermore, it is essential to propose enhancements for its application in smart-grid cybersecurity and to explore alternative MCDM techniques for evaluating security options in smart grids.