Education
Ph.D. in Computer Science, University of Toledo, USA
M.S. in Computer Science, University of Toledo, USA
Bachelor of Technology in Computer Science, India
Background
Dr. Medha Pujari is an Assistant Professor of Computer Science in the Department of Computer Science and Information Technology, College of Arts and Sciences, at Western New England University. She earned her Ph.D. degree in 2023, and M.S. degree in 2019, from the University of Toledo, Ohio. Her research interests are in cybersecurity, network security, in particular, intrusion detection. She worked on several research projects during her graduate studies, some of which were supported by the Department of Energy (DOE), and the Ohio Department of Transportation (ODOT). Dr. Pujari’s current research work is based on intrusion detection in the light of adversarial machine learning.
Journal Publications
A Comparative Study on the Impact of Adversarial Machine Learning Attacks on Contemporary Intrusion Detection Datasets (Link)
Pujari, M., Pacheco, Y., Cherukuri, B., & Sun, W. (2022). A Comparative Study on the Impact of Adversarial Machine Learning Attacks on Contemporary Intrusion Detection Datasets. SN Computer Science, 3(5), 412.
Peer-reviewed Conference Publications
An approach to improve the robustness of machine learning based intrusion detection system models against the carlini-wagner attack (Link)
Pujari, M., Cherukuri, B. P., Javaid, A. Y., & Sun, W. (2022, July). An approach to improve the robustness of machine learning based intrusion detection system models against the carlini-wagner attack. In 2022 IEEE International Conference on Cyber Security and Resilience (CSR) (pp. 62-67). IEEE.
Vehicle classification, rumble strips detection, and mapping using artificial intelligence (Link)
Subedi, R., Shrestha, P., Pujari, M., & Chou, E. Y. (2022). Vehicle classification, rumble strips detection, and mapping using artificial intelligence. In International Conference on Transportation and Development 2022 (pp. 46-56).
A comparative study on contemporary intrusion detection datasets for machine learning research (Link)
Dwibedi, S., Pujari, M., & Sun, W. (2020, November). A comparative study on contemporary intrusion detection datasets for machine learning research. In 2020 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 1-6). IEEE.
PortableVN: A Generic Mobile Application for Network Security Testbeds (Link)
Pujari, M., Narayanamoorthy, J., Sun, W., Jahan, F., McCreary, B., Niyaz, Q., & Devabhaktuni, V. K. (2019). PortableVN: A Generic Mobile Application for Network Security Testbeds. In Proceedings of the International Conference on Security and Management (SAM) (pp. 125-131). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
