### Existing Research  
There is extensive research on cyberattacks and cybersecurity in smart grids, with numerous studies focusing on understanding the threats, vulnerabilities, and countermeasures. Researchers have developed various models and frameworks to assess the potential cyber threats and risks in smart grids [16, 17]. These studies aim to identify the attack vectors, vulnerabilities, and potential consequences to guide the development of effective cybersecurity strategies. Research has been conducted on developing advanced techniques for detecting and preventing cyberattacks in smart grids [18, 19]. This includes anomaly detection algorithms [20, 21], intrusion detection systems (IDS) [22, 23], machine-learning-based approaches [24, 25], and data analytics to detect and respond to suspicious activities in real time [26, 27]. Securing communication channels and implementing robust authentication mechanisms are crucial for protecting smart-grid infrastructure [28]. The research has focused on encryption algorithms [29], secure protocols [30], access control mechanisms [31, 32], and authentication schemes to ensure the confidentiality, integrity, and authenticity of the data transmitted in smart grids [33, 34]. The researchers have explored resilient system architectures for smart grids to mitigate the impact of cyberattacks [35, 36]. This includes decentralized architectures [37, 38], redundancy [39, 40], distributed control systems [41, 42], and fault-tolerant designs to ensure continuous operation and quick recovery from cyber incidents [43]. The studies have analyzed the existing security standards and regulations applicable to smart grids and proposed improvements [44]. The researchers have also examined the gaps and challenges in standardization efforts [45], aiming to establish comprehensive security guidelines [46] and best practices specific to smart-grid deployments [47, 48]. Understanding the role of insider threats [22, 49] and human factors [50, 51] in smart-grid cybersecurity is an emerging area of research. These studies investigate the impact of human behavior [52, 53], social engineering [54, 55], training and awareness programs [56, 57], and policy frameworks to address the human element in cybersecurity [58, 59]. Given the large amount of data generated by smart grids, the research has focused on secure data management, privacy-preserving techniques, and anonymization approaches to protect sensitive information while enabling data analysis for grid optimization and energy management [60, 61]. The research has explored incident response and recovery strategies in smart grids [62, 63]. This includes developing effective incident handling procedures, recovery mechanisms, backup systems, and disaster recovery plans to minimize the impact of cyber incidents and ensure the rapid restoration of services. The research has investigated the importance of threat intelligence and information sharing mechanisms to enhance cybersecurity in smart grids. This includes the development of collaborative platforms, information sharing networks, and sharing protocols to facilitate timely and effective responses to emerging cyber threats [64, 65]. There is a growing body of research on the application of artificial intelligence (AI) in cybersecurity for smart grids [66]. AI techniques, such as machine learning (ML) [67] and deep learning [68, 69], have shown promise in enhancing the security of smart-grid systems. The researchers have explored the use of AI algorithms to detect and predict cyber threats in smart grids. ML models are trained on historical data to identify patterns and anomalies that indicate potential attacks or malicious activities [70, 71]. AI-based anomaly detection techniques are being developed to identify unusual behavior or deviations from normal operations in smart grids. These methods leverage machine learning algorithms to establish baseline behavior and detect abnormal activities that may signify a cyberattack [72, 73].