Professor  Dipankar Dasgupta

Dr. Dipankar Dasgupta is a Professor of Computer Science at the University of Memphis since January 1997. He is a leading figure in the application of bio-inspired and machine learning approaches to cyber defense. His groundbreaking works, including digital immunity, negative authentication, cloud insurance model, and Auth-Spectrum, have earned recognition in Computer World Magazine and other media outlets. With over 300 publications (including 4 patents), having 23000+ citations, and an h-index of 68 as per Google Scholar, Dr. Dasgupta's multidisciplinary research is highly acclaimed. He has received numerous awards, including 2023 Presidential Award for Lifetime Achievement in Research, 2012 Willard R. Sparks Eminent Faculty Award and the 2014 ACM SIGEVO Impact Award. Among other awards: 2007 University’s College of Arts and Sciences awarded the Dunavant Professorship, a 3-year award for exceptional teaching, scholarship, service, and outreach achievement; twice received the College of Arts & Sciences Distinguished Research Award (2002, 2006); the Sigma Xi Research Paper Award (2003); Early Career Research Award (1999), and Dr. Pat E. Burleson’s Professorship (2007). Dr. Dasgupta’s research was supported through several funding sources during last 30 years. He also received five best paper awards in different international conferences and has been organizing Symposium on Computational Intelligence in Cyber Security at IEEE SSCI since 2007. Dr. Dasgupta is an IEEE Fellow, and NAI Fellow, an ACM Distinguished Speaker (2015-2020 and 2026-2028), an IEEE Distinguished Lecturer (2022-2024) and NSF-Fulbright Distinguished Scholar, 2024). He regularly serves as a panelist and keynote speaker and offers tutorials at leading computer science conferences and has given more than 350 invited talks in different universities and industries.
As the founding Director of the Center for Information Assurance (CfIA) at the University of Memphis, he has played a key role in education, training, and outreach activities on AI based cyber security and Information Assurance since 2004.
 

• AI for Security and Security of AI
  Artificial Intelligence (AI) constitutes an umbrella of techniques, and has proven to provide flexible, adaptable solutions to wide variety of security solutions. These techniques typically...
• Context-Aware Adaptive Multi-Factor Authentication
  Authentication and access control are merging in continuous authorization of online resources/services by users and IoTs. In this talk, I will first discuss an adaptive multi-factor...
• Secure Federated Learning: Challenges and mitigations
  Federated Learning (FL) promises collaborative decentralized intelligence without centralized data sharing, although its distributed design introduces some security challenges. This talk...
• Security issues of Generic Large Language Models
  Generic Large Language Models (GLLMs) are continuously being released with increased size and capabilities, promoting the abilities of these tools as universal problem solvers.  While...
• Using AI Agents in Intrusion Detection: Historical perspective
  Intrusion/anomaly detection is an important part of cyber security. This is the process of identifying computer or network activity that is malicious or unauthorized. Most of the intrusion...
• What should GAN in AI stand for?
  GANs were introduced as AI framework where two learning models (a generator and a discriminator) compete in a two-player game. Goodfellow et al. (2014) were the first to explicitly define...