Farnaz Seyyed Mozaffari
Papers
1
Total Citations
24
H-Index
1
About
Dr. Farnaz Seyyed Mozaffari is a leading researcher at the intersection of cybersecurity and cyber-physical systems (CPS), with a particular focus on safeguarding critical infrastructure. Her work centers on developing intelligent, learning-based frameworks for anomaly detection, addressing the unique challenges of securing complex, interconnected systems where failures can have catastrophic real-world consequences. Her most-cited paper, "Learning Based Anomaly Detection in Critical Cyber-Physical Systems" (2020), has garnered 24 citations, establishing a foundational approach for integrating machine learning with CPS security. This work is notable for its practical emphasis on detecting subtle, stealthy attacks that evade traditional rule-based defenses, offering a scalable solution for power grids, water treatment plants, and other vital networks. Dr. Mozaffari’s contributions are pivotal in advancing resilient, autonomous security mechanisms, making her a key voice in the evolving field of CPS protection. Her research continues to influence both academic inquiry and real-world deployment strategies for securing our most essential systems.
Research Focus
Key Achievements
Top Papers
- 1Learning Based Anomaly Detection in Critical Cyber-Physical Systems24 citations · 2020