SD-PAR5ER Security Framework for Smart Drones Video Surveillance Applications

The SD-PAR5ER Security Framework is a patented solution designed in collaboration with Starview Technologies to enhance policy-driven access control and resilience in software-defined 5G/6G environments through real-time traffic classification.

Project Deliverables/Outcomes/Impact:

1 Patent Filed by industry
P. Chuan Alvin, P. Murali Mohan, P. L. Kok Keong, V. Balachandran and C. Y.Joo, "DQN-based Adaptive Network Architecture for High-Fidelity NGN Flow Classification," 2024 IEEE Conference on Communications and Network Security (CNS), Taipei, Taiwan, 2024, pp. 1-7, doi:10.1109/CNS62487.2024.10735584.
L. W. S. Wilson, J. Y. J. Kiat, N. Z. H. Buddy, K. G. S. Mei, A. P. C. Alvin and P. M.Mohan, "Optimizing Network-Level Forensics with Optuna-Generated CNN Model," TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON),Singapore, Singapore, 2024, pp. 1499-1502, doi:10.1109/TENCON61640.2024.10902935.

Architectural diagram of a 5G network featuring a Distributed Controller Architecture, MEC Edge Cloud, and Core Cloud. It illustrates end-to-end network slicing (E2E Slice 1 and 2) managed by an SDN Orchestrator and MANO framework, supporting SD-RAN and P4 SDN Data planes.

Performance dashboard showing training accuracy and loss graphs across 50 episodes, transitioning from an exploration to an exploitation region. Below the graphs is a summary of a Distributed Denial of Service (DDoS) dataset containing nearly 880,000 samples, followed by specific neural network architecture configurations and their resulting accuracy scores.

It leverages programmable security mechanisms to dynamically adapt to evolving threats in smart drones while maintaining network performance and compliance.

Admissions AY2026

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