Academic Profile

Kuo-Hui Yeh serves as a professor at the Institute of Artificial Intelligence Innovation, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. Prior to this appointment, he was a professor in the Department of Information Management at National Dong Hwa University, Hualien, Taiwan, from February 2012 to January 2024. Dr. Yeh earned his M.S. and Ph.D. degrees in Information Management from the National Taiwan University of Science and Technology, Taipei, Taiwan, in 2005 and 2010, respectively. He has contributed over 150 articles to esteemed journals and conferences, covering a wide array of research interests such as IoT security, Blockchain, NFC/RFID security, authentication, digital signatures, data privacy and network security. Furthermore, Dr. Yeh plays a pivotal role in the academic community, serving as an Associate Editor (or Editorial Board Member) for several journals, including the Journal of Information Security and Applications (JISA), Human-centric Computing and Information Sciences (HCIS), Digital Health, Symmetry, Journal of Internet Technology (JIT) and CMC-Computers, Materials & Continua. In the professional realm, Dr. Yeh is recognized as a fellow of the British Computer Society (BCS) and a Senior Member of IEEE, and holds memberships with BCS, (ISC)², ISA, ISACA, CAA, and CCISA. His professional qualifications include certifications like CISSP, CISM, Security+, ISO 27001/27701/42001 Lead Auditor, IEC 62443-2-1 Lead Auditor, and ISA/IEC 62443 Cybersecurity Expert, covering fundamentals, risk assessment, design, and maintenance specialties.

Editorial Roles

No Editorial Roles

This user currently does not serve as an editor for any ICCK journals.

ICCK Publications

Total Publications: 2
Open Access | Research Article | 28 December 2025
Enhancing UAV Security with GPS Spoofing and Jamming Anomaly Detection
Journal of Reliable and Secure Computing | Volume 1, Issue 1: 54-67, 2025 | DOI: 10.62762/JRSC.2025.368867
Abstract
Unmanned aerial vehicles face GPS spoofing and jamming that can compromise navigation and safety. We present an anomaly detection method that achieves both high accuracy and interpretability, enabling UAV operators to understand why an alert is triggered, which enables timely responses and builds trust in autonomous detection systems operating in safety-critical environments. We use five classifiers, including XGBoost, Support Vector Machine, K-Nearest Neighbor, Random Forest, and Naive Bayes, trained on a UAV dataset containing 3622 samples for spoofing detection and 6445 for jamming detection made in PX4 and Gazebo with benign flight and attack cases. After feature scaling and reduction, X... More >

Graphical Abstract
Enhancing UAV Security with GPS Spoofing and Jamming Anomaly Detection
Open Access | Editorial | 29 September 2025
Inaugural Editorial for the Journal of Reliable and Secure Computing
Journal of Reliable and Secure Computing | Volume 1, Issue 1: 1-3, 2025 | DOI: 10.62762/JRSC.2025.220451
Abstract
Presents the introductory editorial for the inaugural issue of this title. More >