Work place: Department of Computer Science and Electrical Engineering, Tarleton State University, Texas A&M University System, TX, USA
E-mail: sadashiva@tarleton.edu
Website: https://orcid.org/0000-0001-9606-0128
Research Interests: Human-Computer Interaction, Computer systems and computational processes, Computational Learning Theory, Parallel Computing, Combinatorial Optimization, Detection Theory
Biography
Dr. G.S. Thejas is an Assistant Professor at the Department of Computer Science and Electrical Engineering at Tarleton State University (TSU), The Texas A&M University System, since 2020. His research areas include Machine Learning, Deep Learning, Click Fraud Detection, Cybersecurity, Human-Computer Interaction (HCI), and Performance Optimization using Parallel Computing. He directs the Machine Intelligence and Security Research Laboratory (MISR Lab). He worked as a trainee for one year at the Defence Research and Development Organization/Electronic and RADAR Development Establishment (DRDO/LRDE). He worked as an Assistant Professor for five years at Siddaganga Institute of Technology (SIT), India. He is a recipient of the Best Graduate Student in Service Award, Dissertation Year Fellowship Award, two times FIU School of Computing and Information Sciences travel Award, FIU Graduate and Professional Student Committee (GPSC) travel grant, and Graduate Assistantship award at FIU. Thejas's research has successfully produced several papers in top conferences and journals like ACM, IEEE, Springer, Elsevier, and MDPI. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM). Elsevier News Board: Based on the work entitled "A hybrid and effective learning approach for Click Fraud detection" has been recognized and appeared in Elsevier's news board as "Curbing the clicking con: The automated detection of click fraud", May 25, 2021.
By Pradeep R. N. R. Sunitha G. S. Thejas
DOI: https://doi.org/10.5815/ijcnis.2023.03.02, Pub. Date: 8 Jun. 2023
A Biometric Authentication Security (BAS) protocol is a method by which a person's unique physiological or behavioral characteristics are used to verify their identity. These characteristics can include fingerprints, facial features, voice patterns, and more. Biometric authentication has become increasingly popular in recent years due to its convenience and perceived security benefits. However, ensuring that the BAS protocols are secure and cannot be easily compromised. . Developing a highly secure biometric authentication protocol is challenging, and proving its correctness is another challenge. In this work, we present a modern mechanism for formally analyzing biometric authentication security protocol by taking a Aadhaar Level-0 Iris-based Authentication Protocol as a use case. The mechanism uses formal methods to formally verify the security of the Aadhaar Level-0 Iris-based Authentication protocol, and is based on the widely-used BAN logic (Buruccu, Abadi, and Needham). Using Scyther model checker we analyze the existing biometric authentication protocol and have shown its effectiveness in identifying potential security vulnerabilities. The proposed mechanism is based on a set of security requirements that must be met for the protocol to be considered secure. These requirements include the need for the protocol to be resistant to replay attacks, man-in-the-middle attacks, and impersonation attacks. The mechanism also considers the possibility of an attacker obtaining the biometric data of a legitimate user.
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