Maria Papaioannou

Maria Papaioannou is a Postdoctoral Researcher at the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark, specializing in cybersecurity engineering with contributions to UN Sustainable Development Goals through secure technology development.

Her research focuses on Machine Learning applications for Internet of Things security, including intrusion detection systems, honeypot optimization, and user-centric authentication. She investigates technical implementations of tiny machine learning for resource-constrained devices while addressing human factors in security adoption, particularly for emerging technologies like passkeys.

Recent publications reveal strong thematic convergence across machine learning-driven security solutions for IoT ecosystems, emphasizing both algorithmic innovation and usability considerations. Her review articles systematically analyze research gaps in intrusion detection scalability, passkey adoption barriers, and adaptive deception technologies, highlighting interdisciplinary connections between cybersecurity, human factors, and embedded systems engineering.