Diana W. Dawoud
Papers
2
Total Citations
93
H-Index
2
About
Diana W. Dawoud is a leading researcher in cybersecurity, specializing in the application of machine learning and reinforcement learning to intrusion detection in communication networks. Her work addresses critical vulnerabilities in modern industrial control systems (ICSs) and the Internet of Things (IoT), where connectivity to external networks introduces new security risks. In her highly cited 2024 review, Dawoud systematically analyzed reinforcement-learning-based intrusion detection methods, providing a comprehensive framework for protecting ICSs against evolving cyber threats—a paper that has already garnered 67 citations. She also developed a lightweight multilayer machine learning detection system for wireless sensor networks (WSNs), achieving 26 citations by offering an efficient solution for low-power IoT environments. Dawoud’s contributions are notable for balancing robust security with practical resource constraints, making her work essential for researchers and practitioners designing resilient communication networks. Her research continues to shape the field of adaptive, AI-driven cybersecurity.
Research Focus
Key Achievements
Top Papers
- 1
- 2