Assistant Professor Priyadarsi Nanda of University of Technology Sydney, Australia

The proliferation of heterogeneous, resource-constrained Internet of Things (IoT) devices across industrial automation, smart infrastructure, healthcare, and precision agriculture has exposed number of fundamental limitations. Existing architectures treat clustering, protocol adaptation, security management, and access control as independent subsystems, producing fragmented solutions which cannot simultaneously sustain scalability, energy efficiency, Quality of Service (QoS), cryptographic integrity, and intelligent privilege enforcement under dynamic and adversarial conditions. In this presentation we will address these interdependent challenges through SecureEdge-IoT; a unified, edge-native integrated framework coordinating adaptive hierarchical clustering, dynamic protocol reconfiguration, standards-compliant end-to-end object security, trust-aware key lifecycle management, and machine learning-assisted proactive access control for constrained IoT edge networks. Our proposed implementations through SEcuRecNet-IoT, RAISEF-IoT, OSCORE-IOT and ML-ACCESS-IOT are designed and implemented on Contiki-NG using up to 111 nodes and demonstrate validation and efficiency achieved. First, we establish the foundation implementing hybrid BIRCH-DBSCAN clustering mechanism with AES-128 encryption and QoS-driven reconfiguration, achieving 43.26% latency reduction, 92% energy efficiency, and 99.91% message delivery success while maintaining security overhead below 5%. In our proposed scheme we also enhance session-based key rotation preserving cryptographic continuity throughout. Second, Security is formally improved through SecuRecNet-IoT, which is a Security Adaptation Convergence Model (SACM) where, security events trigger reconfiguration and reconfiguration triggers rekeying supported by hierarchical trust propagation across device, cluster, and global levels, reducing key exposure by 41% over 24 continuous hours. Next, RAISEF-IoT unifies cross-layer coordination by embedding trust-integrated Enhanced Clustering Features and synchronising all adaptation mechanisms through explicit approval gates, achieving 100% detection on all 500 injected attacks across four attack classes. Next, OSCORE-IoT extends RFC 8613, end-to end object security to the MQTT publish-subscribe model for the first time, providing broker-transparent confidentiality through domain-separated HKDF keys, an LRU context cache, and a formally proven four-step synchronisation protocol maintaining nonce uniqueness across all protocol transitions achieving 3.8% security overhead, 88% reduction versus DTLS 1.2, and 100% replay prevention across 15 independent runs. Finally, ML-ACCESS-IoT closes the access-window problem through a Random Forest classifier deployed exclusively at the edge gateway, mapping an eight-dimensional trust-trend feature vector to four graduated access states every 15 seconds with zero client overhead. SecureEdge-IoT delivers a complete, formally grounded solution from adaptive clustering through machine learning-assisted access control establishing a principled and deployable foundation for secure, efficient, and intelligently governed next generation heterogeneous IoT edge networks.

Biography:

Dr. Priyadarsi Nanda is an Associate Professor at the University of Technology Sydney (UTS) with more than 36 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), Sybil attack detection in IoT based applications, intelligent firewall design. In Cybersecurity research, he has published over 150 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 24 HDR at UTS (20 PhD + 4 Masters), and currently, supervising 14 more PhD students. Dr. Nanda is a Senior MemberofIEEE.

Address:Australia