Daniel Commey

Papers

Total Citations

H-Index

About

Daniel Commey is a researcher at the forefront of cybersecurity for emerging technologies, with a primary focus on the Internet of Robotic Things (IoRT) and federated learning. His most cited work, "Securing the Internet of Robotic Things: A Federated Learning Approach" (2024), tackles the critical challenge of Distributed Denial of Service (DDoS) attacks in interconnected robotic systems. In this study, Commey systematically evaluates the performance of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs) within a federated learning framework, demonstrating how decentralized machine learning can enhance threat detection while preserving data privacy. Though early in his career, this work has already garnered attention for its practical implications in securing autonomous systems. Commey’s contributions lie at the intersection of artificial intelligence and network security, offering scalable solutions for safeguarding IoRT environments. His research is particularly relevant for students and engineers working on resilient robotic networks, as it provides a blueprint for integrating privacy-preserving AI into real-world security protocols. With a growing citation count, Commey is establishing himself as a promising voice in the field of intelligent system defense.