Daniel T. Ramotsoela

Daniel T. Ramotsoela is an academic researcher at the University of Pretoria's Department of Electrical, Electronic and Computer Engineering, within the College of Engineering, Built Environment and Information Technology. His research focuses on cybersecurity applications for critical infrastructure, particularly water distribution systems and industrial control environments. With over 30 publications spanning from 2015 to 2024, he has established himself as a significant contributor to the fields of intrusion detection, machine learning applications in critical infrastructure protection, and wireless sensor network security.

Dr. Ramotsoela's research interests center around applying machine learning techniques to enhance security in critical infrastructure systems. His work particularly emphasizes water distribution networks, where he has published extensively on anomaly detection methods using neural networks and other AI approaches. He also investigates security challenges in 5G networks, industrial environments, and microgrids, with a strong focus on practical implementation challenges. His recent work shows an increasing emphasis on reinforcement learning applications for network resource allocation and admission control in next-generation communication systems.

Analysis of his publication trends reveals a consistent focus on applying machine learning to infrastructure security problems, with a clear progression from foundational surveys to more sophisticated technical implementations. His early work (2017-2019) established survey papers on anomaly detection in industrial wireless sensor networks, particularly using water systems as case studies. From 2020 onward, his research became more specialized, addressing specific challenges like behavioral intrusion detection, data imputation in sensor networks, and biometric authentication systems for industrial applications. The most recent publications (2023-2024) demonstrate advanced applications of deep reinforcement learning and multi-agent systems for 5G network optimization and security.