Ttile: GA-Based User Identity Management
Abstract: Authentication with multiple factors is an emerging trend to secure access to the sensitive information of an organization. Multi-factor Authentication (MFA) being used to genuinely identify authorized users through an authentication process via passwords, security tokens, biometrics, cognitive behavior metrics, software/hardware sensors, etc. Existing MFA systems typically use static policies for selecting authentication factors and do not consider dynamic aspects of the operating environment. We developed a GA-based authentication and identity management framework for adaptive selection of multiple modalities at different operating environment so to make authentication strategy unpredictable to the hackers. This methodology, called adaptive multi-factor authentication (A-MFA) incorporates a novel approach of calculating trustworthy values of different authentication factors while being used under different user environmental settings. Accordingly, MOGA-based application is developed to determine an appropriate subset of authentication factors (at triggering events) on the fly thereby leaving no exploitable a priori pattern or clue for adversaries. Empirical studies are conducted with varying environmental settings and the performance of the adaptive MFA is compared with other selection strategies. These results reflect that such a methodology of adaptive authentication can provide legitimacy to user transactions with an added layer of access protection that is not rely on a fixed set of authentication modalities. Robustness of the system is assured by designing the GA-based framework in such a way that if any modality data get compromised, the system can still perform flawlessly using other non-compromised modalities and different operating modes. Scalability can also be achieved by adding new and/or improved modalities with existing set of modalities and integrating the operating/configuration parameters for the added modality. A Patent (#9,912,657, approved on March 6, 2018) on Adaptive Multi-Factor Authentication System is approved/allowed on November 2017.
Dr. Dipankar Dasgupta has been based at the University of Memphis, Tennessee as a professor of Computer Science since 1997. Prof. Dasgupta is the recipient of “2012 Willard R. Sparks Eminent Faculty Award” the uppermost recognition given to a faculty member by the University. He holds his study interests largely in the area of scientific computing, design, and development of intelligent cyber security solutions inspired by biological processes. He has made priceless contributions in applying bio-inspired approaches to numerous complications in cyber security. Dr. Dasgupta is at the lead of research in applying bio-inspired approaches to cyber defense. He also served as a program co-chair at the National Cyber Leap Year Summit that was organized at the request of the White House Office of Science and Technology Directorate. He is one of the founding fathers of the field of artificial immune systems, in which he has established himself. Dr. Dasgupta has been Advisory Board of Geospatial Data Center (GDC) at Massachusetts Institute of Technology (MIT) since 2010. He has received Best Paper Award in several international conferences in the years 1996, 2006, 2009, 2011 and 2013. He owns more than 220 publications in book chapters, journals, and international conferences which have been cited extensively. His Google Scholars profile indicates more than 4,500 citations since 2013.