Design, analysis, and evaluation of highly secure smart city infrastructures and services

Abstract

Critical infrastructure resources and services, such as energy networks, water treatment facilities, and 5G telecommunications, form the backbone of national security and public welfare. However, many of these infrastructures rely on outdated technologies, rendering them increasingly vulnerable to evolving cyber threats. As these infrastructures become increasingly digitized and integrated under Industry 4.0 - integrating cloud computing, Artificial Intelligence (AI), and the Industrial Internet of Things (IIoT) - they simultaneously introduce a broader attack surface susceptible to threats such as sensor spoofing, Denial-of-Service (DoS), and man-in-the-middle attacks. Existing critical infrastructure testbeds are isolated and limited in their ability to replicate cross-domain dependencies and security vulnerabilities inherent in modern smart cities. To address this gap, this dissertation developed a Federated Cybersecurity Testbed as a Service (FCTaaS) environment, an innovative approach that integrates geographically dispersed critical infrastructure testbeds to enable the development and experimentation of effective algorithms to secure the normal operations of critical infrastructures against a wide range of cyberattacks. The research specifically focused on two critical infrastructures: The Water Treatment Facility Testbed (WTFT) and the 5G Telecommunication Testbed (5GTT). It begins with a comprehensive threat modeling across Industrial Control Systems (ICS) and 5G architecture to identify vulnerabilities, followed by designing and implementing security detection and mitigation algorithms. Specifically, we have developed an edge-deployed anomaly detection algorithm that is based on an autoencoder that achieved 98.3% accuracy in detecting cyberattacks against water treatment infrastructure. We have also demonstrated the effectiveness of our security defense algorithms in detecting cyberattacks against 5G networks with an accuracy of 98.9% against various cellular network attacks. This dissertation developed a unified and scalable cybersecurity research environment that significantly facilitates the development of realistic critical infrastructure experimentations and AI-driven security algorithms to secure and protect their normal operations against any type of cyberattacks known or unknown, regardless of their origins, insider or outsider.