Dr. Monika Roopak is a Lecturer in Cyber Security at the University of Bedfordshire. She completed her Ph.D. in Cyber Security in IoT Networks at Newcastle University, UK, and has since gained a wealth of experience across academia, industry, and collaborative research projects. Dr. Roopak’s core research areas include cybersecurity in IoT systems, physical layer security, threat modelling, intrusion detection systems (IDS), and the integration of secure communication mechanisms within critical infrastructure. Her skill set encompasses deep learning, unsupervised learning, machine learning, and image processing for applications in both cyber and physical security domains.

Building on her extensive research portfolio, Dr. Roopak has explored the use of channel state information (CSI) for physical-layer authentication in Wi-Fi sensing, the behavior of wireless power transfer systems in eddy current testing, and novel zero-day DDoS detection mechanisms using unsupervised learning. She has also contributed significantly to digital forensics and AI-driven policy decision-making frameworks. Her work demonstrates a unique ability to bridge theoretical cybersecurity models with applied technologies in wireless systems, non-destructive testing, and embedded device security.

She is proficient with a wide array of technical tools, including Python, Keras, TensorFlow, MATLAB, .NET, and embedded platforms such as the NVIDIA Jetson and HPC, which support both her research and teaching efforts. Dr. Roopak’s current and future research interests lie at the intersection of cyber-physical system security, adversarial machine learning, explainable AI (XAI), and privacy-preserving forensics. She is particularly interested in expanding into federated learning, trustworthy AI for IoT networks, and secure wireless power-enabled sensing.

Her research career includes diverse roles, such as a Post-Doctoral Research Associate, where she contributed to EPSRC-funded projects on the secure Internet of Energy, and as a Research Fellow in digital forensics funded by DSTL, helping advance cyber investigation tools and techniques. Dr. Roopak’s strong analytical and problem-solving skills make her adept at addressing emerging security challenges in dynamic environments.

Her contributions have been recognized through several prestigious awards, including the Best Paper Award at ICAPS 2023, the IET Networks Premium Award in 2021, and Best Paper at IEEE CCWC 2020. These honors reflect her commitment to impactful, interdisciplinary research. Dr. Roopak’s blend of technical acumen, collaborative spirit, and forward-thinking approach make her a valuable asset to any research initiative focused on securing the future of connected systems.

• Cybersecurity in IoT networks,
• Physical Layer Security,
• Intrusion Detection Systems,
• Zero-day Attack Detection,
• Wireless Power Rransfer Security,
• Non-destructive testing (NDT),
• Wi-Fi Sensing and CSI-based Authentication,
• Unsupervised and Deep Learning,
• Adversarial Machine Learning,
• Explainable AI (XAI),
• Federated Learning,
• Privacy-preserving Forensics,
• AI-driven Decision Support Systems

• Computer Networks and Security
• Databases And Computer Networks
• Cybercrime and Security
• Advanced Networks
• Wireless Communication
• Artificial Intelligence