Dipankar Dasgupta

Abstract: An Adaptive Multi-Factor Authentication (A-MFA) Methodology

Multi-factor Authentication (MFA) is the current trend to genuinely identify authorized users (in multiple ways) through an authentication process via passwords, security token, biometrics, cognitive behavior metric, software/hardware sensors, etc. Existing MFA systems typically use static policies for selecting authentication factors and do not consider dynamic aspects of operating environment. We are developing an authentication framework for adaptive selection of multiple modalities at different operating environments so to make authentication strategy unpredictable to the hackers. This methodology, called adaptive multi-factor authentication (A-MFA) will incorporate a novel approach of calculating trustworthy values of different authentication factors while use under different user environmental settings. The objectives of this project are to develop (i) a trust-based adaptive, robust and scalable software-hardware framework in selecting authentication modalities for continuous and triggered authentication, (ii) optimal algorithms to determine the security parameters of each authentication modality/factor. Accordingly, a subset of authentication factors will be determined (at triggering events) on the fly thereby leaving no exploitable a priori pattern or clue for adversaries.  Empirical studies are conducted with varying environmental parameters and the performance of the adaptive MFA is compared with other selection strategies. The empirical results reflects that such a methodology of adaptive authentication can provide legitimacy to user transactions with an added layer of access protection that will not rely on a fixed set of authentication modalities. Robustness of the system will be maintained through designing the framework so that if any modality data get compromised, the system will still perform flawlessly using other non-compromised modalities. Scalability will be guaranteed by adding new and/or improved modalities with existing set of modalities and generating the operating/configuration parameters for the added modality.


Dipankar Dasgupta is a Professor of Computer Science at the University of Memphis. His research interests are broadly in the area of scientific computing, design, and development of intelligent cyber security solutions inspired by biological processes. He contributed remarkably in applying bio-inspired approaches to various problems in cyber security. He is one of the founding fathers of the field of artificial immune systems, in which he has established himself. His latest book, “Immunological Computation”, is a graduate level textbook, was published by CRC press in 2009. He also edited two books: one on Evolutionary Algorithms in Engineering Applications and the other is entitled “Artificial Immune Systems and Their Applications”, published by Springer-Verlag. His new textbook on Advances in User Authentication will be published by Springer-Verlag, 2016.

Dr. Dasgupta is at the forefront of research in applying bio-inspired approaches to cyber defense, served as a program co-chair at the National Cyber Leap Year Summit organized at the request of the White House Office of Science and Technology Directorate (2009). Some of his groundbreaking works, like digital immunity, negative authentication, and cloud insurance model, put his name in Computer World Magazine and other News media. Prof. Dasgupta is an Advisory Board member of Geospatial Data Center (GDC), Massachusetts Institute of Technology since 2010, and worked on joint research projects with MIT.