Dr. Jing Li, University Technology Malaysia, China
🎓 Dr. Jing Li is pursuing his PhD in Computer Science at University Technology Malaysia (UTM) since 2021. He holds a Master’s in Information Management from ZheJiang University and a Bachelor’s in Computer Science from China JiLiang University. With over 15 years in the ICT industry, he specializes in networking, cybersecurity, and IoT. His research interests span IoT security, digital forensics, big data, and machine learning. Dr. Li has authored several publications in prestigious journals and is an active member of IEEE. He is also proficient in AI-based scientific research tools
Publication profile
Scopus
Education 🎓
Dr. Jing Li is currently pursuing his PhD in Computer Science at University Technology Malaysia (UTM), where he also holds an International Doctoral Scholarship. He earned his Master’s degree in Information Management from ZheJiang University and a Bachelor’s in Computer Science from China JiLiang University, Hangzhou.
Professional Experience 💼
Dr. Li has held roles including Technical Co-founder at Hangzhou Yunmei Technology Co., Ltd., Product Architect at ArcSoft (Hangzhou) Technology Co., Ltd., and Software Engineer at Aerohive Networks, inc. His expertise spans networking, cybersecurity, IoT, and machine learning.
Research Focus
Dr. Jing Li’s research focuses on enhancing IoT security through advanced machine learning techniques. His work primarily explores feature selection and reduction methods for improving intrusion detection systems in IoT environments. Through critical reviews and comparative studies, Dr. Li aims to optimize classification models, contributing significantly to the fields of cybersecurity and digital forensics. His research, published in prestigious journals like the Journal of Big Data and Intelligent Systems with Applications, underscores his expertise in applying AI-driven solutions to mitigate IoT security risks. Dr. Li’s efforts are pivotal in advancing the understanding and implementation of robust security measures in interconnected systems. 🔒
Publication Top Notes
Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning
Enhancing IoT security: A comparative study of feature reduction techniques for intrusion detection system
A critical review of feature selection methods for machine learning in IoT security