AI-Enhanced Cybersecurity Framework for IoT Devices in Smart Cities

Thesis Abstract

The rapid proliferation of Internet of Things (IoT) devices within smart city infrastructures has significantly enhanced urban management efficiency but concurrently heightened vulnerabilities to cyber threats, necessitating robust and adaptive cybersecurity solutions. This study aims to develop an AI-enhanced cybersecurity framework specifically tailored for IoT devices deployed in smart city environments, seeking to bridge existing gaps in threat detection, response timeliness, and system resilience. The primary objectives encompass (1) assessing the current cybersecurity challenges faced by IoT devices in smart cities, (2) designing an AI-driven intrusion detection and prevention system (IDPS) integrated into the existing network architecture, (3) evaluating the framework’s effectiveness through empirical testing, and (4) establishing guidelines for sustainable AI-based cybersecurity practices in urban settings. A mixed-methods research design is employed, integrating qualitative and quantitative approaches to ensure comprehensive analysis. The qualitative component involves semi-structured interviews with 25 cybersecurity experts and city infrastructure managers to identify prevalent vulnerabilities, threat vectors, and operational constraints. The quantitative component consists of deploying a prototype AI-enhanced IDPS within a simulated smart city environment comprising 1,000 IoT devices across various sectors such as transportation, utilities, and public safety. Data collection instruments include network traffic logs, system alerts, and intrusion attempt records, alongside surveys measuring system usability and trustworthiness among end-users. The mixed-methods approach facilitates triangulation, enhancing the reliability and validity of findings. Data analysis is conducted using advanced statistical and machine learning techniques. Quantitative data undergoes descriptive statistics, correlation analysis, and performance evaluation through metrics such as detection rate, false positive rate, and response time. Specifically, supervised learning classifiers like Random Forest and Support Vector Machines will be used to develop predictive models for anomaly detection, while unsupervised clustering algorithms identify novel threat patterns. Qualitative data from interviews are analyzed thematically using NVivo to extract insights on implementation challenges and user acceptance, guided by the Technology Acceptance Model (TAM) and Situational Awareness Theory. Expected findings indicate that the AI-enhanced framework significantly improves detection accuracy, reduces false positives, and accelerates threat response times compared to traditional rule-based systems. The integration of machine learning algorithms demonstrates higher adaptability to evolving cyber threats and reduces reliance on manual rule updates. Insights from expert interviews underscore the importance of contextual customization and user training to optimize system performance and trust. These findings will substantiate the hypothesis that AI integration enhances IoT cybersecurity in complex, dynamic urban environments. This research contributes novel empirical evidence towards the efficacy of AI-driven cybersecurity solutions in smart city contexts and advances theoretical understanding by applying models such as TAM and Situational Awareness in the deployment of AI systems for cybersecurity. The developed framework offers a scalable and adaptable model that urban policymakers, technologists, and cybersecurity practitioners can adopt, modify, and extend in diverse urban settings. The study also highlights critical considerations for ethical AI deployment, including data privacy, transparency, and user acceptance, emphasizing the need for inclusive design strategies. In conclusion, the study affirms that AI-enhanced cybersecurity frameworks can substantially mitigate IoT vulnerabilities in smart cities by providing proactive, intelligent, and scalable threat management solutions. Recommendations include adopting integrated AI-based IDPS systems across urban infrastructure, establishing continuous training and awareness programs for stakeholders, and fostering collaborative efforts among city authorities, technology providers, and researchers to refine and standardize cybersecurity practices. Future research should explore real-time deployment in operational urban environments, the integration of blockchain technologies for enhanced data integrity, and the examination of AI explainability to foster greater user trust and compliance. This research lays a foundation for sustainable and resilient smart city ecosystems equipped to counter the rapidly evolving landscape of cyber threats.