HR job title
- Associate Professor
- Contact Info
- Websites
Kim, Jinoh Associate Professor
Positions
- Associate Professor, Computer Science and Information Systems, Texas A&M University - Commerce - (Commerce, Texas, United States)
- TEES Researcher at TAMU-Commerce, TEES Regional Divisions, Texas A&M Engineering Experiment Station (TEES)
I am an Associate Professor in the Department of Computer Science and Information Systems at the Texas A&M University-Commerce since Fall 2012. My research interests spans systems and networks in computer science, including distributed systems, network monitoring, measurements and management, big-data computing and analytics, and high-performance computing.
- Overview
- Publications
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Overview
Publications
selected publications
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academic article
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Shao, R., Sim, A., Wu, K., & Kim, J.
(2023).
Leveraging History to Predict Infrequent Abnormal Transfers in Distributed Workflows. Sensors.
23(12), 5485-5485.
-
Kim, C., Chang, S., Kim, J., Lee, D., & Kim, J.
(2023).
Automated, Reliable Zero-day Malware Detection based on Autoencoding Architecture. IEEE Transactions on Network and Service Management.
PP(99), 1-1.
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Fan, W., Hong, H., Kim, J., Wuthier, S., Nakashima, M., Zhou, X., Chow, C., & Chang, S.
(2023).
Lightweight and Identifier-Oblivious Engine for Cryptocurrency Networking Anomaly Detection. IEEE Transactions on Dependable and Secure Computing.
20(2), 1302-1318.
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Kim, C., Chang, S., Lee, D., Kim, J., Park, K., & Kim, J.
(2023).
Reliable Detection of Location Spoofing and Variation Attacks. IEEE Access.
11, 10813-10825.
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Chang, S., Park, K., Kim, J., & Kim, J.
(2023).
Securing UAV Flying Base Station for Mobile Networking: A Review. 15(5), 176-176.
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Kim, J., Nakashima, M., Fan, W., Wuthier, S., Zhou, X., Kim, I., & Chang, S.
(2022).
A Machine Learning Approach to Anomaly Detection Based on Traffic Monitoring for Secure Blockchain Networking. IEEE Transactions on Network and Service Management.
19(3), 3619-3632.
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Nakashima, M., Sim, A., Kim, Y., Kim, J., & Kim, J.
(2021).
Automated Feature Selection for Anomaly Detection in Network Traffic Data. ACM Transactions on Management Information Systems.
12(3), 1-28.
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Baek, S., Kwon, D., Suh, S. C., Kim, H., Kim, I., & Kim, J.
(2021).
Clustering-based label estimation for network anomaly detection. Digital Communications and Networks.
7(1), 37-44.
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Choi, J., Gu, Y. u., & Kim, J.
(2020).
Learning-based dynamic cache management in a cloud. Journal of Parallel and Distributed Computing.
145, 98-110.
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Kwon, D., Kim, H., Kim, J., Suh, S. C., Kim, I., & Kim, K. J.
(2019).
A survey of deep learning-based network anomaly detection. Cluster Computing.
22(Suppl 1), 949-961.
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Kim, J., & Sim, A.
(2019).
A New Approach to Multivariate Network Traffic Analysis. Journal of Computer Science and Technology.
34(2), 388-402.
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Tharp, J., Suh, S. C., Cho, H., & Kim, J.
(2019).
Improving signature quality for network application identification. Digital Communications and Networks.
5(3), 139-146.
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Kim, J., Sim, A., Tierney, B., Suh, S., & Kim, I.
(2019).
Multivariate network traffic analysis using clustered patterns. Computing.
101(4), 339-361.
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Malaiya, R. K., Kwon, D., Suh, S. C., Kim, H., Kim, I., & Kim, J.
(2019).
An Empirical Evaluation of Deep Learning for Network Anomaly Detection. IEEE Access.
7, 140806-140817.
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Chou, J., Lai, T., Kim, J., & Rotem, D.
(2015).
Exploiting Replication for Energy-Aware Scheduling in Disk Storage Systems. IEEE Transactions on Parallel and Distributed Systems.
26(10), 2734-2749.
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Kang, K., & Kim, J.
(2015).
A Case Study on Converged Security with Event Correlation of Physical and Information Security. International Journal of Security and Its Applications.
9(9), 77-94.
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Kim, S., Kim, J., & Weissman, J. B.
(2014).
A Security-enabled Grid System for MINDS Distributed Data Mining. Journal of Grid Computing.
12(3), 521-542.
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Moon, I., Albalawi, U., Kim, J., Suh, S., & Lee, W.
(2014).
A Hybrid Classifier with a Binning Method for Network Application Identification. Journal of Integrated Design and Process Science.
18(2), 3-22.
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Kim, J., Chou, J., & Rotem, D.
(2014).
iPACS: Power-aware covering sets for energy proportionality and performance in data parallel computing clusters. Journal of Parallel and Distributed Computing.
74(1), 1762-1774.
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Tharp, J., Kim, J., Suh, S. C., & Cho, H.
(2013).
Reconciling Multiple Matches for the Signature-Based Application Identification. Journal of Communications.
8(12), 883-892.
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Kim, J., & Rotem, D.
(2012).
FREP: Energy proportionality for disk storage using replication. Journal of Parallel and Distributed Computing.
72(8), 960-974.
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Kim, J., Chandra, A., & Weissman, J. B.
(2011).
Passive Network Performance Estimation for Large-Scale, Data-Intensive Computing. IEEE Transactions on Parallel and Distributed Systems.
22(8), 1365-1373.
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Kim, J., Chandra, A., & Weissman, J. B.
(2009).
Using Data Accessibility for Resource Selection in Large-Scale Distributed Systems. IEEE Transactions on Parallel and Distributed Systems.
20(6), 788-801.
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Purification, S., Kim, J., Kim, J., Kim, I., & Chang, S.
Distributed and Lightweight Software Assurance in Cellular Broadcasting Handshake and Connection Establishment. Electronics.
12(18), 3782-3782.
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Shao, R., Sim, A., Wu, K., & Kim, J.
(2023).
Leveraging History to Predict Infrequent Abnormal Transfers in Distributed Workflows. Sensors.
23(12), 5485-5485.
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chapter
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Raavi, M., Wuthier, S., Sarker, A., Kim, J., Kim, J., & Chang, S.
(2022).
Towards Securing Availability in 5G: Analyzing the Injection Attack Impact on Core Network. Communications in Computer and Information Science.
Silicon Valley Cybersecurity Conference.
(pp. 143-154).
Springer Nature.
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Kanuri, N. S., Chang, S., Park, Y., Kim, J., & Kim, J.
(2022).
Impact of Location Spoofing Attacks on Performance Prediction in Mobile Networks. Communications in Computer and Information Science.
Silicon Valley Cybersecurity Conference.
(pp. 107-119).
Springer Nature.
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Suh, S. C., Bandi, H., Kim, J., & Tanik, U. J.
(2020).
Case Study: STEM Contribution in Indian IT Clusters. STEM in the Technopolis: The Power of STEM Education in Regional Technology Policy.
(pp. 285-296).
Springer Nature.
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Kim, J., Chou, J., & Rotem, D.
(2011).
Energy Proportionality and Performance in Data Parallel Computing Clusters. Lecture Notes in Artificial Intelligence.
Scientific and Statistical Database Management.
(pp. 414-431).
Springer Nature.
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Weissman, J. B., Kumar, V., Chandola, V., Eilertson, E., Ertoz, L., Simon, G., Kim, S., & Kim, J.
(2007).
DDDAS/ITR: A Data Mining and Exploration Middleware for Grid and Distributed Computing. Lecture Notes in Artificial Intelligence.
Computational Science ICCS 2007.
(pp. 1222-1229).
Springer Nature.
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Raavi, M., Wuthier, S., Sarker, A., Kim, J., Kim, J., & Chang, S.
(2022).
Towards Securing Availability in 5G: Analyzing the Injection Attack Impact on Core Network. Communications in Computer and Information Science.
Silicon Valley Cybersecurity Conference.
(pp. 143-154).
Springer Nature.
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conference paper
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Kwon, D., Neagu, R., Rasakonda, P., Ryu, J. T., & Kim, J.
(2023).
Evaluating Unbalanced Network Data for Attack Detection. 23-26.
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Fan, W., Kim, J., Kim, I., Zhou, X., & Chang, S.
(2021).
Performance Analyses for Applying Machine Learning on Bitcoin Miners. 1-4.
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Weinger, B., Kim, J., Sim, A., Nakashima, M., Moustafa, N., & Wu, K. J.
(2020).
Enhancing IoT Anomaly Detection Performance for Federated Learning. 206-213.
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Kim, J., Sim, A., Kim, J., & Wu, K.
(2020).
Botnet Detection Using Recurrent Variational Autoencoder. 1-6.
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Al Olaimat, M., Lee, D., Kim, Y., Kim, J., & Kim, J.
(2020).
A Learning-based Data Augmentation for Network Anomaly Detection. Proceedings - International Conference on Computer Communications and Networks, ICCCN.
1-10.
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Nakashima, M., Sim, A., & Kim, J.
(2020).
Evaluation of Deep Learning Models for Network Performance Prediction for Scientific Facilities. 53-56.
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Kim, T., Gu, Y. u., & Kim, J.
(2020).
QoS-enabled Cache Architecture for a Private Cloud. 434-439.
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Kim, J., Sim, A., Kim, J., Wu, K., & Hahm, J.
(2020).
Transfer Learning Approach for Botnet Detection Based on Recurrent Variational Autoencoder. 41-47.
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Syal, A., Lazar, A., Kim, J., Sim, A., & Wu, K.
(2019).
Automatic Detection of Network Traffic Anomalies and Changes. 3-10.
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Kim, J., Sim, C., & Choi, J.
(2019).
Generating Labeled Flow Data from MAWILab Traces for Network Intrusion Detection. 45-48.
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Cetin, B., Lazar, A., Kim, J., Sim, A., & Wu, K.
(2019).
Federated Wireless Network Intrusion Detection. 6004-6006.
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Kwon, D., Natarajan, K., Suh, S. C., Kim, H., & Kim, J.
(2018).
An Empirical Study on Network Anomaly Detection using Convolutional Neural Networks. International Conference on Distributed Computing Systems.
1595-1598.
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Kim, J., Choi, J., & Sim, A.
(2018).
Spatio-temporal Analysis of HPC I/O and Connection Data. International Conference on Distributed Computing Systems.
1585-1588.
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Chan-Tin, E., Kim, T., & Kim, J.
(2018).
Website Fingerprinting Attack Mitigation using Traffic Morphing. International Conference on Distributed Computing Systems.
1575-1578.
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Malaiya, R. K., Kwon, D., Kim, J., Suh, S. C., Kim, H., & Kim, I.
(2018).
An Empirical Evaluation of Deep Learning for Network Anomaly Detection. 893-898.
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Kim, T., Suh, S. C., Kim, H., Kim, J., & Kim, J.
(2018).
An Encoding Technique for CNN-based Network Anomaly Detection. 2960-2965.
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Kim, J., & Sim, A.
(2017).
A New Approach to Online, Multivariate Network Traffic Analysis. 1-6.
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Baek, S., Kwon, D., Kim, J., Suh, S. C., Kim, H., & Kim, I.
(2017).
Unsupervised Labeling for Supervised Anomaly Detection in Enterprise and Cloud Networks. 205-210.
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Kim, J., Sim, A., Suh, S. C., & Kim, I.
(2016).
An Approach to Online Network Monitoring Using Clustered Patterns. 656-661.
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Kim, J., Yoo, W., Sim, A., Suh, S. C., & Kim, I.
(2016).
A Lightweight Network Anomaly Detection Technique. 896-900.
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Kumar, A., Kim, J., Suh, S. C., & Choi, G.
(2015).
Incorporating Multiple Cluster Models for Network Traffic Classification. Conference on Local Computer Networks.
185-188.
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Kim, J., Dong, B., Byna, S., & Wu, K.
(2015).
Security for the Scientific Data Services Framework. 1871-1875.
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Kim, J., Moon, I., Lee, K., Suh, S. C., & Kim, I.
(2015).
Scalable Security Event Aggregation for Situation Analysis. 14-23.
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Kim, J., & Rotem, D.
(2011).
Energy proportionality for disk storage using replication. 81-92.
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Chou, J., Kim, J., & Rotem, D.
(2011).
Energy-Aware Scheduling in Disk Storage Systems. 423-433.
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Kim, J., Abbasi, H., Chacn, L., Docan, C., Klasky, S., Liu, Q., ... Wu, K.
(2011).
Parallel In Situ Indexing for Data-intensive Computing. 65-72.
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Kim, J., Chandra, A., & Weissman, J. B.
(2008).
Accessibility-based Resource Selection in Loosely-coupled Distributed Systems * *This work was supported in part by National Science Foundation grant ITR-0325949. 777-784.
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Kim, J., Chandra, A., & Weissman, J.
(2007).
Exploiting Heterogeneity for Collective Data Downloading in Volunteer-based Networks. 275-282.
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Budati, K., Kim, J., Chandra, A., & Weissman, J.
(2007).
NGS: Service Adaptation in Open Grid Platforms. 1-8.
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Kim, G., Kim, J., & Na, J.
(2005).
Design and Implementation of Policy Decision Point in Policy-Based Network. 534-538.
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Kim, J., Kang, K., Na, J., Kim, I., Kim, K., Jang, J., & Sohn, S.
(2003).
Practical Network Attack Situation Analysis using Sliding Window Cache Scheme. 1038-1041.
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Kwon, D., Neagu, R., Rasakonda, P., Ryu, J. T., & Kim, J.
(2023).
Evaluating Unbalanced Network Data for Attack Detection. 23-26.
Background
education and training
- Ph.D. in Computer Science, University of Minnesota - (Minneapolis, Minnesota, United States) 2010
- M.S. in Computer Science, Inha University - (Incheon, South Korea) 1994
- B.E. in Computer Science, Inha University - (Incheon, South Korea) 1991
Contact
first name
- Jinoh
has last name
- Kim
Other
- Jinoh.Kim@tamuc.edu