Assoc. Prof. Priyadarsi Nanda,University of Technology Sydney (UTS), Australia
Dr. Priyadarsi Nanda is an Associate Professor at the University of Technology Sydney (UTS) with more than 34 years of experience. He is a strong researcher specialising research and development in a vast range of topics; Cybersecurity, IoT security, Internet Traffic Engineering, wireless sensor network security and many more related areas. His most significant work has been in the area of Intrusion detection and prevention systems (IDS/IPS) using image processing techniques, Sybil attack detection in IoT based applications, intelligent firewall design. In Cybersecurity research, he has published over 130 high quality refereed research papers including Transactions in Computers, Transactions in Parallel Processing and Distributed Systems (TPDS), Future Generations of Computer Systems (FGCS) as well as many ERA Tier A/A* conference articles. Dr. Nanda has successfully supervised 23 HDR at UTS (20 PhD + 3 Masters), and currently, supervising 15 more PhD students.
Speech Title: Deployment Of Critical Infrastructures In Industrial Internet Of Things (IIoT) Using Fog Computing And Zero-Trust Model
Abtract: Rapid digitisation of industrial assets has led to the widespread adoption of the Industrial Internet of Things (IIoT) across various sectors, enhancing monitoring, connectivity, and operational efficiency. IIoT replaces traditional Industrial Control Systems (ICS) with smarter, more interactive devices that communicate with next-generation IT systems. These systems are deployed within critical infrastructure environments, where security and reliability are paramount. Traditional security models, such as Defence in Depth, rely on security zoning and implicit trust within designated zones. However, this approach introduces vulnerabilities, allowing malicious actors to traverse defences and move laterally within trusted environments. To address these security challenges, Zero-Trust Architecture (ZTA) has been explored and will be presented as a robust framework that assumes all assets within an environment may be compromised. Zero-Trust enforces continuous evaluation of user and system attributes, applying dynamic security controls based on contextual information. We will explore the use of static thresholds and machine learning within zero-trust utilising anomaly detection and variable weighting techniques to dynamically adjust trust levels and access permissions. The framework is evaluated using the TON IoT network dataset to determine the most effective anomaly detection algorithm for real-time security enforcement. By combining Zero-Trust security principles with Fog Computing, this talk will present a comprehensive framework that enhances the security, reliability, and performance of IIoT environments.