Version 1
: Received: 14 April 2024 / Approved: 15 April 2024 / Online: 15 April 2024 (10:12:25 CEST)
How to cite:
Yunis, M.; Khalil, A.; Sammouri, W. Towards a Conceptual Framework for AI-Driven Anomaly Detection in Smart City IoT Networks for Enhanced Cybersecurity. Preprints2024, 2024040924. https://doi.org/10.20944/preprints202404.0924.v1
Yunis, M.; Khalil, A.; Sammouri, W. Towards a Conceptual Framework for AI-Driven Anomaly Detection in Smart City IoT Networks for Enhanced Cybersecurity. Preprints 2024, 2024040924. https://doi.org/10.20944/preprints202404.0924.v1
Yunis, M.; Khalil, A.; Sammouri, W. Towards a Conceptual Framework for AI-Driven Anomaly Detection in Smart City IoT Networks for Enhanced Cybersecurity. Preprints2024, 2024040924. https://doi.org/10.20944/preprints202404.0924.v1
APA Style
Yunis, M., Khalil, A., & Sammouri, W. (2024). Towards a Conceptual Framework for AI-Driven Anomaly Detection in Smart City IoT Networks for Enhanced Cybersecurity. Preprints. https://doi.org/10.20944/preprints202404.0924.v1
Chicago/Turabian Style
Yunis, M., Ayman Khalil and Wissam Sammouri. 2024 "Towards a Conceptual Framework for AI-Driven Anomaly Detection in Smart City IoT Networks for Enhanced Cybersecurity" Preprints. https://doi.org/10.20944/preprints202404.0924.v1
Abstract
This paper presents a theoretical framework aimed at improving IoT network cybersecurity by AI-driven anomaly detection in the context of rapid urbanization and the development of smart cities. The framework draws upon the Complex Adaptive Systems Theory, Theory of Planned Behavior, the Technology Acceptance Model, and the Socio-Technical Systems Theory, and intri-cately examines how AI might be integrated into the intricate ecosystem of smart cities to detect and mitigate cyber threats. It emphasizes AI-driven anomaly detection methods as pivotal in-struments that impact cybersecurity enhancement in smart city infrastructures. The proposed framework also highlights the crucial role that human-related factors, including user behavior and adoption, play in determining how effective AI applications are in enhancing cybersecurity in smart cities. It also recognizes the important moderating roles played by outside factors like legislative frameworks, environmental contexts, and technological breakthroughs in the relationship between AI-enabled anomaly detection and enhanced cybersecurity. The goal of this research is to shed light on the interactions between technology and socio-technical dynamics by offering a thorough theoretical understanding of AI's potential in smart city cybersecurity. It highlights the significance of taking into account a variety of contextual aspects when employing AI to strengthen the digital security of urban areas, and provides a strategic direction for future research and practical implementation.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
