Biography
Yue Zhang is an assistant professor at Drexel University’s Computer Science department. Before joining Drexel, Yue Zhang was working as a Postdoc in the Department of Computer Science and Engineering at The Ohio State University. He obtained Ph.D. (2020) in Information Security from Jinan University. His research primarily focuses on system security, specifically in the areas of IoT Security and mobile security. He has published more than 40 papers in security conferences (e.g., USENIX Security, ACM CCS, and NDSS) and journals (e.g., TDSC, TPDS). He received a Best Paper Honorable Mention Award at ACM CCS 2022, and the Best Paper Award at 2019 IEEE International Conference on Industrial Internet. He has also served on the organization committees of the conferences (e.g., track chair for IEEE MSN) and technical program committee of the conferences (e.g., USENIX Security, NDSS, ACM CCS, RAID). He serves as an editor for the Blockchain Journal, Electronics Journal, and Computers, Materials & Continua Journal. His research had led to the discovery of many vendor-acknowledged vulnerabilities, such as by Bluetooth SIG, Apple, Google, and Texas Instruments, and had attracted intense media attention such as Hacker News, and Mirage News.
Research Interest
Bluetooth Security, IoT Security, Mobile Security

Editor
Work Details
Assistant Professor
Drexel University
Department of Computer Science and Engineering
USA
Contribution by Topic Area
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Which articles are now trending?
Research Articles
- On how Doping with Atoms of Gadolinium and Scandium affects the Surface Structure of Silicon
- Potentially Toxic Metals in Cucumber Cucumis sativus Collected from Peninsular Malaysia: A Human Health Risk Assessment
- Investigation and Energy Modeling of New Generation Environmentally Friendly Energy Source Thorium Fueled Molten Salt Reactors
- Evaluating Digital Imaging Technologies for Anogenital Injury Documentation in Sexual Assault Cases
- Challenge and Readiness to Implemented Geothermal Energy in Indonesia
- Enhancing Material Property Predictions through Optimized KNN Imputation and Deep Neural Network Modeling
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