Hybrid Firewalls and AI-Powered Threat Intelligence in Smart Cities
DOI:
https://doi.org/10.15662/IJEETR.2022.0404001Keywords:
Hybrid Firewalls, AI-Powered Threat Intelligence,, Smart Cities, Cybersecurity, Machine Learning, Anomaly Detection, Internet of Things (IoT), Network Security, Threat Detection, Automated ResponseAbstract
Smart cities integrate advanced digital infrastructure and Internet of Things (IoT) devices to enhance urban living, but this interconnectedness introduces complex cybersecurity challenges. Protecting critical infrastructure and sensitive data from increasingly sophisticated cyber threats necessitates robust and adaptive security solutions. Hybrid firewalls, which combine stateful inspection and application-layer filtering, alongside Artificial Intelligence (AI)-powered threat intelligence, offer a promising approach to safeguarding smart city networks. This paper explores the integration of hybrid firewall architectures with AI-driven threat detection and response mechanisms in the context of smart cities. It highlights how AI techniques, including machine learning and deep learning, can enhance real-time threat detection, anomaly identification, and automated response, complementing traditional firewall capabilities. The literature review examines prior developments in firewall technology and AI applications in cybersecurity. The research methodology involves a mixed-methods approach, combining experimental implementation, simulation of attack scenarios, and performance evaluation. Key findings suggest that hybrid firewalls integrated with AI can significantly improve detection accuracy, reduce false positives, and enable proactive defense strategies in complex urban networks. The paper outlines a detailed workflow for deploying such systems within smart city frameworks and evaluates their advantages, including scalability and adaptability, as well as disadvantages such as computational overhead and data privacy concerns. Results indicate that this combined approach provides superior protection against evolving threats compared to conventional firewalls. The paper concludes by emphasizing the need for continued research on optimizing AI algorithms for threat intelligence and integrating privacy-preserving techniques. Future work focuses on real-world deployment challenges and expanding AI capabilities to cover emerging cyber-physical threats, aiming to secure the digital foundation of smart cities effectively.
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