Protecting Public Safety and Critical Infrastructure Systems in Smart Cities via Cybersecurity: AI- Based Threat Detection and Response

Smart cities are evolving rapidly (enhanced public safety, improved infrastructure management and optimized urban services) as IoT and automation technologies are embraced in urban spaces worldwide. Yet this very transformation exposes critical infrastructure systems to risks of cyberattacks unprecedented in scale and in nature. That said, safeguarding public safety within smart cities — especially at critical infrastructure sectors such as energy, transportation, health care, and emergency response — requires proactive measures against emerging cyber threats against both physical and digital infrastructures. With this AI-based threat detection and response system, critical infrastructure in smart cities can be developed and secured. This study focuses on extending traditional cybersecurity frameworks with cutting-edge machine learning (ML) and deep learning techniques for immediate anomaly detection, predictive threat modeling, and automated response actions. This enables the system to identify new and advanced attack vectors, such as zero-day exploits, Distributed Denial of Service (DDoS) attacks, and malware intrusions, with greater accuracy and speed than traditional rule-based approaches through the use of AI models across various techniques, including supervised and unsupervised learning methods. The proposed system can operate in various aspects of smart city infrastructure management, such as IoT networks, communication systems, and centralized control. We further explore federated learning's potential to enhance collaborative threat intelligence exchange among critical infrastructure operators while preserving data privacy and security. Moreover, we advocate for resilience via automated response capabilities that reduce the effects of cyberattacks by quickly isolating impacted systems, minimizing damage and enabling rapid recovery. This study illustrates the implementation of AI-driven cybersecurity solutions in the smart city domain insimulation models and case studies related to power grids, smart healthcare systems, and traffic management systems. These findings underscore the effectiveness of leveraging AI-driven methods for detecting sophisticated attack behavior, minimizing false positives, and improving the resilience of critical infrastructure. We also discuss other challenges too— adversarial attacks on AI models, scalability of the system, and ethical concerns regarding usage of data and automation of decision-making based on AI models. These results have important implications at the city-and state-level, as they affirm the need for the adoption of AI-based threat detection and response systems within the cybersecurity frameworks of smart cities to maintain the operational continuity and security of public safety services and other critical infrastructure. The results augment current initiatives seeking to better protect urban environments from the growing onslaught of cyber threats and help enable safer and more sustainable smart cities in the digital era.