Jinoh Kim, Ph.D.

Professor

Along with his role as an associate professor of computer science at A&M-Commerce, Dr. Jinoh Kim is an affiliate faculty scientist at Lawrence Berkeley National Laboratory (LBNL) and a Silicon Valley Cybersecurity Institute (SVCSI) member. Before returning to academia, he was a senior researcher at ETRI, a national lab in Korea, working on broadband networking systems and security since 1991.

He transitioned to academia with a vision of innovative, in-depth research and learning in networking and security with talented students and colleagues in the open community. His research goal lies in providing greater reliability and predictability in networked systems with complex functionality and components, utilizing machine intelligence and algorithmic methodologies.

Office Hours

Monday at 12 – 2 p.m. and Wednesday at 11 a.m. – 2 p.m.

A Conversation with Dr. Kim

What has been your favorite course to teach?

I enjoy teaching networking and security courses in the Internet of Things (IoT) era with an ever-increasing number of devices and their connectivity. The networking course explores the concepts of network algorithms, protocols and architecture, with their rationale and intuitions to understand the complexity of modern computer networks. The security course focuses on the principles of security objectives and functions, with practical measures against emerging threats and vulnerabilities in the computing world with growing connectivity.

Tell us about a project you are currently working on or recently completed.

Our research group has explored security concerns in the 6G mobile communication setting with the University of Colorado Colorado Springs since 2021. We particularly focus on aerial base station security, which will be increasingly utilized in the new mobile communications settings, with two research challenges: (1) potential malware attacks against aerial base stations and (2) location integrity essential for mobile entities for accurate dispatch and placement. This project produced two journal articles (IEEE Transactions on Network and Service Management and IEEE Access) and three conference papers (including IEEE Big Data 2021 and IEEE Conference on Machine Learning and Applications 2023). Computer Science graduate student Chiho Kim received the first place award at the 17th annual Texas A&M University System (TAMUS) Pathways Student Research Symposium in 2022 with his work on this project.

Educational Background

• Ph.D., Computer Science, University of Minnesota, 2010
• M.S., Computer Science, Inha University, 1994
• B.E., Computer Science and Engineering, Inha University, 1991

Awards and Honors

• Research Mentor of the Year, Texas A&M University-Commerce, 2022
• Finalist for Best Paper, International Conference on Blockchain and Cryptocurrency, Institute of Electrical and Electronics Engineers, 2021
• Nominee, H.M. Lafferty Distinguished Faculty Award for Scholarship and Creative Activity, Texas A&M University-Commerce, 2018, 2020
• Visiting Faculty Program, U.S. Department of Energy, 2015, 2016
• Outstanding Contributions Award, Society for Design and Process Science, 2014

Academic Positions

• Assistant Professor, Texas A&M University-Commerce, 2012-present
• Assistant Professor, Lock Haven University of Pennsylvania, 2011-2012

Research Interests

• Network security, monitoring and management
• Machine learning, systems/network telemetry and analytics
• Distributed systems, data-intensive computing and scientific computing

Professional Organizations

• Steering Committee, International Workshop on Systems and Network Telemetry and Analytics
• Senior member, Institute of Electrical and Electronics Engineers
• Member, Association for Computer Machinery
• Member, Korean-American Scientists and Engineers Association

Research Funding

• $148,857, University of Colorado Colorado Springs, 2021-2024
• $141,000, ETRI, 2017-2019
• $48,000, ETRI, 2016-2017
• $193,723, Sysmate Inc., 2012-2015
• $122,000, ETRI, 2013-2015

Selected Publications

• Chiho Kim, Sang-Yoon Chang, Jonghyun Kim, Dongeun Lee, Jinoh Kim, “Automated, Reliable Zero-day Malware Detection based on Autoencoding Architecture,” IEEE Transactions on Network and Service Management (TNSM), Vol. 20, Issue. 3, pp. 3900–3914, September 2023
• Chiho Kim, Sang-Yoon Chang, Dongeun Lee, Jonghyun Kim, Kyungmin Park, Jinoh Kim, “Reliable Detection of Location Spoofing and Variation Attacks,” IEEE Access, January 2023
• Minji Kim, Dongeun Lee, Kookjin Lee, Doowon Kim, Sangman Lee, Jinoh Kim, “Deep Sequence Models for Packet Stream Analysis and Early Decisions,” In Proceedings of the IEEE 47th Conference on Local Computer Networks (LCN'22), Edmonton, Canada, September 2022
• Jinoh Kim, Makiya Nakashima, Wenjun Fan, Simeon Wuthier, Xiaobo Zhou, Ikkyun Kim, Sang-Yoon Chan, “A Machine Learning Approach to Anomaly Detection based on Traffic Monitoring for Secure Blockchain Networking,” IEEE Transactions on Network and Service Management (TNSM), Vol. 19, No. 3, pp. 3619–3632, September 2022
• Makiya Nakashima, Alex Sim, Youngsoo Kim, Jonghyun Kim, Jinoh Kim, “Automated Feature Selection for Anomaly Detection in Network Traffic Data,” ACM Transactions on Management Information Systems (TMIS), Vol. 12, No. 3, pp. 1-28, June 2021