About this Research Topic
The rapid digitalization of energy infrastructures, particularly through the development of smart grids, represents a significant technological advancement in the energy sector. Smart grids leverage advanced metering infrastructure, real-time data analytics, and automated control systems to enhance efficiency, reliability, and sustainability of energy distribution. However, this increased connectivity also amplifies vulnerability to cyber threats, as these systems become attractive targets for cybercriminals and nation-state actors. Traditional cybersecurity measures are insufficient to address the complex and evolving threats faced by modern smart grids. Artificial intelligence (AI) offers a promising solution, enabling proactive threat detection, anomaly identification, and automated response mechanisms. By integrating AI into cybersecurity strategies, the energy sector can better protect its critical infrastructure against emerging cyber threats.
The integration of digital technologies in energy infrastructures, particularly smart grids, has revolutionized the energy sector by enhancing efficiency, reliability, and sustainability. However, this digital transformation has also introduced significant cybersecurity challenges. Smart grids, with their extensive networks of interconnected devices, automated control systems, and real-time data exchanges, are highly susceptible to cyber threats. Traditional security measures are inadequate to address the sophisticated and evolving nature of these cyber-attacks. The problem lies in the increasing complexity and interdependency of smart grid systems, which require advanced, adaptive security solutions capable of real-time threat detection and response. Cyber-physical attacks, where attackers target both digital and physical components, pose a significant risk, potentially leading to large-scale disruptions and severe economic and national security consequences. Additionally, the ethical implications and privacy concerns associated with AI deployment in these systems further complicate the landscape. This research aims to address these critical issues by exploring AI-driven cybersecurity solutions, strategies for secure AI implementation, and the development of comprehensive resilience frameworks to protect smart grids from diverse and sophisticated threats.
This Research Topic invites contributions that explore the application of artificial intelligence (AI) in enhancing the cybersecurity of smart grids. Authors are encouraged to submit original research, case studies, and review articles that address the following themes:
- AI Algorithms for Anomaly Detection: Development and validation of AI algorithms for identifying and responding to cyber threats in smart grids.
- Secure AI Deployment: Strategies and challenges in implementing AI technologies securely within critical energy infrastructures.
- Cyber-Physical Security: Innovative approaches to protect both digital and physical components of smart grids.
- Machine Learning for Threat Intelligence: Leveraging machine learning to improve threat intelligence and predictive cybersecurity.
- Blockchain for Smart Grid Integrity: Use of blockchain technology to enhance security and transparency in smart grid transactions and data management.
By focusing on these themes, the special issue aims to foster a comprehensive understanding of AI-driven solutions for smart grid security.
The integration of digital technologies in energy infrastructures, particularly smart grids, has revolutionized the energy sector by enhancing efficiency, reliability, and sustainability. However, this digital transformation has also introduced significant cybersecurity challenges. Smart grids, with their extensive networks of interconnected devices, automated control systems, and real-time data exchanges, are highly susceptible to cyber threats. Traditional security measures are inadequate to address the sophisticated and evolving nature of these cyber-attacks. The problem lies in the increasing complexity and interdependency of smart grid systems, which require advanced, adaptive security solutions capable of real-time threat detection and response. Cyber-physical attacks, where attackers target both digital and physical components, pose a significant risk, potentially leading to large-scale disruptions and severe economic and national security consequences. Additionally, the ethical implications and privacy concerns associated with AI deployment in these systems further complicate the landscape. This research aims to address these critical issues by exploring AI-driven cybersecurity solutions, strategies for secure AI implementation, and the development of comprehensive resilience frameworks to protect smart grids from diverse and sophisticated threats.
This Research Topic invites contributions that explore the application of artificial intelligence (AI) in enhancing the cybersecurity of smart grids. Authors are encouraged to submit original research, case studies, and review articles that address the following themes:
- AI Algorithms for Anomaly Detection: Development and validation of AI algorithms for identifying and responding to cyber threats in smart grids.
- Secure AI Deployment: Strategies and challenges in implementing AI technologies securely within critical energy infrastructures.
- Cyber-Physical Security: Innovative approaches to protect both digital and physical components of smart grids.
- Machine Learning for Threat Intelligence: Leveraging machine learning to improve threat intelligence and predictive cybersecurity.
- Blockchain for Smart Grid Integrity: Use of blockchain technology to enhance security and transparency in smart grid transactions and data management.
By focusing on these themes, the special issue aims to foster a comprehensive understanding of AI-driven solutions for smart grid security.
Keywords: AI-enhanced security, Smart grids, Cyber resilience, Energy infrastructure, Threat detection systems
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
