Computer Science

Networking Lab

The Networking and Cybersecurity Lab provides resources for hands-on learning in network design and configuration, system administration, cybersecurity operations, digital forensics, vulnerability assessment, and security monitoring.

Classrooms & Walk-In Labs

The Division of Mathematics and Computer Science is housed in the G. B. Hodge Center. Classrooms are equipped with ceiling-mounted video-data projectors, Wi-Fi, and document cameras. In addition, there are three computer-equipped classrooms with 24 student workstations each. In two labs, computers have access to the campus virtual computing environment, which includes the Microsoft Office 365 suite and all of the mathematics and computer science software used in classes.

The cybersecurity lab supports hands-on instruction in system administration, network configuration, cyber defense, vulnerability assessment, digital forensics, and security monitoring. High-performance student workstations with 10-core CPUs and 64GB DDR5 RAM, and a Nvidia RTX A4000 graphics cards allow students to run multiple virtual machines, isolated operating systems, servers, and security tools simultaneously.

The lab supports exercises involving Windows and Linux administration, virtualization, packet analysis, firewall configuration, intrusion detection, penetration testing, log analysis, malware investigation, and incident response. Configurable switches, routers, wireless equipment, and segmented lab networks allow students to design, secure, attack, monitor, and troubleshoot realistic network environments without affecting campus production systems.

Undergraduate Research Lab

Undergraduate students who assist faculty with research benefit tremendously, graduating with stronger analytical and communication skills, deeper disciplinary knowledge, and improved professional work habits. Faculty also benefit by expanding the scope of their research programs and providing students opportunities to pursue exploratory projects that may inform future research directions.

To support these efforts, we are developing a dedicated research laboratory that will house specialized instrumentation and provide collaborative space for students to work closely with faculty mentors and peers.

The Computer Science faculty at the University of South Carolina Upstate actively pursue several areas of research, including:

• Artificial intelligence / machine learning
• Cybersecurity / cyber defense
• Data science / big data analytics
• IoT / edge computing
• UAV networks / drone swarms
• Smart systems / digital twins
• Wireless sensor networks
• Robotics / automation
• Bioinformatics / computational biology
• Medical cyber-physical systems
• Smart grid security
• Intrusion and malware detection

The following are a sampling of publications that highlight recent research by faculty members:
[1.] Q. Zeng, Y. Fu, and F. Nait-Abdesselam, “FedGraph-ID: A federated graph learning framework for intrusion detection in UAV networks under adversarial settings,” in Proceedings of the IEEE Conference on Computer Communications (INFOCOM), Tokyo, Japan, 2026.

[2.] A. J. A. Majumder, A. J. C. Mohammad, N. Nguyen, and G. T. Lewis, “Adaptive and scalable cryptography for IoT-enabled medical cyber-physical systems: Benchmarking symmetric and asymmetric approaches,” in Proceedings of the 50th IEEE Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, 2026.

[3.] W. Zhong, X. Zhang, and N. Yu, “Tree based diffusion transformers for improving intrusion detection performance,” in Proceedings of the 9th International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, 2026.

[4.] Q. Zeng and F. Nait-Abdesselam, “Enhancing UAV network security: A human-in-the-loop and GAN-based approach to intrusion detection,” IEEE Internet of Things Journal, Feb. 2025.

[5.] J. Mulo, H. Liang, M. Qian, M. Biswas, B. Rawal, Y. Guo, and W. Yu, “Navigating challenges and harnessing opportunities: Deep learning applications in internet of medical things,” Future Internet, vol. 17, no. 3, p. 107, 2025.

[6.] M. Qian, A. A. Musa, M. Biswas, Y. Guo, W. Liao, and W. Yu, “Survey of artificial intelligence model marketplace,” Future Internet, vol. 17, no. 1, p. 35, 2025.

[7.] M. Qian, C. Qian, and W. Yu, “Edge intelligence in smart agriculture CPS,” in Edge Intelligence in Cyber-Physical Systems, Academic Press, 2025, pp. 265–291.

[8.] A. J. A. Majumder, J. Gibb, A. Kuchi, and R. S. S. Aveti, “Advancing intelligent energy harvesting in IoT networks using agentic and GenAI approaches,” in Proceedings of the 28th IEEE International Conference on Computer and Information Technology (ICCIT), Bangladesh, 2025.

[9.] A. J. A. Majumder, C. Veilleux, A. Kuchi, and R. S. S. Aveti, “IoT sensor node security threats analytics: An AI approach,” in Proceedings of the 49th IEEE Annual Computers, Software, and Applications Conference (COMPSAC), Toronto, Canada, 2025.

[10.] Q. Zeng, A. Bashir, and F. Nait-Abdesselam, “UAVIDS-2025: A benchmark dataset for intrusion detection in UAV networks using machine learning techniques,” in Proceedings of the IEEE Conference on Communications and Network Security (CNS), Avignon, France, 2025.

[11.] C. Ripley, M. Qian, Y. Wang, and W. Yu, “Applicability of MongoDB for smart city digital twin implementation,” in Proceedings of the 23rd IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA), 2025, pp. 338–341.

[12.] W. Zhong, X. Zhang, and N. Yu, “Clustering diffusion model for improving protein structure prediction performance,” in Proceedings of the 8th International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, 2025.

[13.] X. Zhang and F. Gu, “Campus parking lot identification with a novel deep neural network,” in Proceedings of the 2nd International Conference on Intelligent Computing and Robotics (ICICR), Dalian, China, Jun. 2025.

[14.] J. Hao, W. Yi, M. Ren, C. Ai, T. Qi, and L. Guo, “Clustering based collaborative learning grouping for knowledge building,” in Proceedings of the APWeb-WAIM Joint International Conference on Web and Big Data, Springer, 2024, pp. 210–223.

[15.] W. Zhong and X. Zhang, “Multi-level generative pretrained transformer for improving malware detection performance,” in Proceedings of the 7th International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, May 2024.

[16.] J. Ma, J. Dai, Y. Liu, M. Han, and C. Ai, “Contrastive learning for rumor detection via fitting beta mixture model,” in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM), 2023, pp. 4160–4164.

[17.] X. Zhang, F. Gu, W. Zhong, and C. Ai, “A hybrid resampling technique with adaptive intervals used in the parallel/distributed particle filters,” in Proceedings of the 7th International Conference on Computer Science and Artificial Intelligence (CSAI), Beijing, China, Dec. 2023, pp. 319–325.

Faculty members regularly mentor undergraduate students on research projects in artificial intelligence, cybersecurity, data science, robotics, networking, and related fields. Many students present their work at the annual USC Upstate Research Symposium and other professional venues. Below are examples of poster or presentation topics by mathematics or computer science students mentored by Computer Science faculty members at the Annual South Carolina Upstate Research Symposium.

[1] S. Chokka, S. Beltran, and A. K. M. J. A. Majumder, “AI-based Security Analytics for Smart Grid Systems,” University of South Carolina Upstate Research Symposium, Apr. 2024.

[2] R. S. S. Aveti, A. Kuchi, J. Gibb, and A. K. M. J. A. Majumder, “Optimizing Energy Harvesting in IoT Networks through GenAI-Driven AIoT Systems,” University of South Carolina Upstate Research Symposium, Apr. 2025.

[3] X. Zhang, F. Doan, A. Inman, and L. Pham, “Improve the Identification Accuracy of Parking Stickers with A Deep Neural Network and Persistent Homology Features,” University of South Carolina Upstate Research Symposium, Apr. 2025.

[4] F. Doan, L. Pham, and X. Zhang, “Enhancing Text-to-Speech Synthesis with Topological Data Analysis and Deep Neural Networks,” University of South Carolina Upstate Research Symposium, Apr. 2025.

[5] A. Inman, X. Zhang, and T. Brown, “Speech Enhancement with a Novel Deep-Learning-Based Model,” University of South Carolina Upstate Research Symposium, Apr. 2025.

[6] A. J. A. Majumder, N. Nguyen, and A. J. C. Mohammad, “Security Analytics of Cryptography for IoT-Enabled Medical Cyber-Physical Systems,” University of South Carolina Upstate Research Symposium, Apr. 2026.