Yahia Baashar

Papers

Total Citations

H-Index

About

Yahia Baashar is a researcher whose work sits at the intersection of cybersecurity, cyber-physical systems (CPS), and artificial intelligence. His most-cited paper, “Deep Learning Model for Cybersecurity Attack Detection in Cyber-Physical Systems” (2022, 13 citations), addresses a critical challenge in modern infrastructure: protecting smart manufacturing, intelligent transportation, and IoT ecosystems from sophisticated cyber threats. By proposing a deep learning-based detection framework, Baashar contributes to making these interconnected systems more resilient against attacks. His research is particularly timely as CPS become increasingly embedded in critical infrastructure, where security failures can have severe real-world consequences. Baashar’s work demonstrates a commitment to bridging the gap between theoretical AI models and practical security solutions, offering tools that can be deployed in real-time environments. While his citation count is still growing, the relevance of his focus area—securing the backbone of smart cities and industrial automation—positions him as an emerging voice in the cybersecurity community. For students and researchers interested in the intersection of machine learning and infrastructure security, Baashar’s work provides a clear example of how deep learning can be harnessed to defend against evolving cyber threats in complex, interconnected systems.