Dr. Donghoon Kim
Professor of Computer Science
About Dr. Donghoon Kim
Donghoon Kim is a Professor of Computer Science at Arkansas State University. He received
his Ph.D. in Computer Science from North Carolina State University. His research interests
include cybersecurity, machine learning, cloud computing, cyber-physical systems,
malware detection, Tor network security, and biomedical data analytics. His work has
been supported by the National Science Foundation and the Arkansas INBRE program.
He has published extensively in journals and conferences and actively mentors undergraduate
and graduate students in cybersecurity and machine learning research.
Education
- Ph.D. Computer Science, North Carolina State University, USA
- M.S. Computer Science, Auburn University, USA
Research at a Glance
Summary
Dr. Kim’s research focuses on cybersecurity, machine learning, cloud computing, and cyber-physical systems. His current work includes Tor network security, malware detection, secure cloud storage, antimicrobial resistance prediction, and security for smart transportation systems. He is particularly interested in applying machine learning and data analytics to address real-world security and biomedical challenges.
Areas of Expertise
- Cybersecurity
- Machine Learning
- Cloud Computing and Security
- Cyber-Physical Systems Security
- Biomedical Data Analytics
Selected Publications
- Kyungtae Kim, Doosung Hwang, and Donghoon Kim ”On the Spatiotemporal Generalization Limits of Website Fingerprinting on Tor v3 Onion Services,” The 35th International Conference on Computer Communications and Networks (ICCCN 2026), Honolulu, Hawaii, USA, July 2026
- Kyungtae Kim, Caiden Chadwick, Doosung Hwang, and Donghoon Kim ”Rapid Real-Time Android Malware Detection Using Opcode Analysis: A Proof-of-Concept Framework,” The 13th IEEE International Conference on Intelligent Mobile Computing 2025 (IMC 2025), 2025
- Yeojin Jung, Namkyeong Kim, and Donghoon Kim ”Improving Antimicrobial Resistance (AMR) Phenotype Prediction for Unseen Bacteria through Data Augmentation and Machine Learning,” The 16th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2025) companion, Philadelphia, PA, October 2025