Congratulations Emmanuel Osei Owusu

Emmanuel Osei Owusu is a graduating PhD student from the Department of Computer Engineering at Kwame Nkrumah University of Science and Technology (KNUST). His research interests include network security, deep learning solutions for resource-constrained systems, Collaborative machine learning and Radio Frequency (RF) fingerprinting for device authentication in Internet of Things (IoT) and edge computing devices.

For his PhD work, he developed a comprehensive deep learning framework to enhance network security in resource-constrained environments such as IoT and industrial control systems. He designed and evaluated lightweight, communication-aware, and adaptive deep learning solutions across three core areas: efficient DDoS detection using Neural Architecture Search (NAS), collaborative anomaly detection using a federated learning framework, and energy-aware device authentication via RF fingerprinting. His work showed great potential for deploying robust security on edge devices, achieving 99.98% accuracy in DDoS detection with minimal latency, reducing federated learning communication costs by over 80%, and cutting energy consumption for device authentication by 17% while maintaining high accuracy.

Emmanuel extends a heartfelt appreciation to his supervisors, Dr Griffith Selorm Klogo, Prof Kwame Osei Boateng and Prof. Emmanuel Kofi Akowuah, for their support and guidance in reaching this milestone.

The KNUST Engineering Education Project (KEEP) has been instrumental in his academic success by providing financial support. He is very grateful for the support and encouragement received from the staff and management of KEEP.

He plans to further work on integrating the model design and RF authentication into scalable collaborative learning to secure IoT networks against impersonation and spoofing attacks, and conducting long-term field evaluations of the proposed solutions.