About this Research Topic
In the realm of modern technology, the integration of the Internet of Everything (IoE) marks a pivotal evolution in various industry sectors, keying into interconnected devices, real-time analytics, and automated controls to elevate operational efficiency, reliability, and sustainability. Yet, this interconnectivity introduces heightened vulnerability as it substantially expands attack surfaces for cybercriminals and nation-state threats. Traditional cyber defense approaches fall short against the backdrop of the sophisticated and dynamic challenges these IoE systems face.
This Research Topic aims to elucidate the integration of Artificial Intelligence (AI) within IoE to forge proactive, intelligent cybersecurity measures. The goal is to develop capabilities for early threat detection, nuanced anomaly identification, and swift automated responses. Through AI’s predictive analytics and cognitive threat intelligence, the research intends to construct a resilient cybersecurity framework capable of defending critical infrastructural systems against the complexities of modern cyber-attacks, while also addressing the ethical and privacy concerns associated with AI technologies.
To gather further insights within the realms of AI-enhanced IoT security, we welcome articles addressing, but not limited to, the following themes:
- AI Algorithms for Anomaly Detection: Innovative development and validation methodologies for AI algorithms tailored to IoT and interconnected systems.
- Secure AI Deployment: Exploring effective strategies and confronting challenges in implementing AI technologies within critical infrastructures.
- Cyber-Physical Security: Cutting-edge solutions for shielding both the digital and physical aspects of IoE and similar smart ecosystems.
- Machine Learning for Threat Intelligence: Enhancements in threat prediction and intelligence across networks via advanced machine learning techniques.
- Blockchain for IoT Integrity: Empowering IoT and smart systems with blockchain to augment data security and transparency.
By exploring these topics, the research aims to shed light on advanced AI-driven strategies to strengthen cybersecurity across the vast landscape of interconnected systems.
This Research Topic aims to elucidate the integration of Artificial Intelligence (AI) within IoE to forge proactive, intelligent cybersecurity measures. The goal is to develop capabilities for early threat detection, nuanced anomaly identification, and swift automated responses. Through AI’s predictive analytics and cognitive threat intelligence, the research intends to construct a resilient cybersecurity framework capable of defending critical infrastructural systems against the complexities of modern cyber-attacks, while also addressing the ethical and privacy concerns associated with AI technologies.
To gather further insights within the realms of AI-enhanced IoT security, we welcome articles addressing, but not limited to, the following themes:
- AI Algorithms for Anomaly Detection: Innovative development and validation methodologies for AI algorithms tailored to IoT and interconnected systems.
- Secure AI Deployment: Exploring effective strategies and confronting challenges in implementing AI technologies within critical infrastructures.
- Cyber-Physical Security: Cutting-edge solutions for shielding both the digital and physical aspects of IoE and similar smart ecosystems.
- Machine Learning for Threat Intelligence: Enhancements in threat prediction and intelligence across networks via advanced machine learning techniques.
- Blockchain for IoT Integrity: Empowering IoT and smart systems with blockchain to augment data security and transparency.
By exploring these topics, the research aims to shed light on advanced AI-driven strategies to strengthen cybersecurity across the vast landscape of interconnected systems.
Keywords: AI-enhanced security, Smart systems, Cyber resilience, Threat detection systems, IoT security
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.
