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Identification of User Behavioral Biometrics for Authentication using Keystroke Dynamics and Machine Learning

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  • Fri, 03/09/2018 - 11:00am - 12:00pm

Identification of User Behavioral Biometrics for Authentication using Keystroke Dynamics and Machine Learning

Computer Science Colloquium Series
Sowndarya Krishnamoorthy

Date:  Friday, March 9th, 2018
Time: 11:00 am
Location: Odette, 110

Abstract: Keystroke dynamics refer to the unique patterns of rhythms and timing based features that are created when a user accesses the screen of the computing devices. The talk focuses on the effective classification of the behaviour of users accessing computing devices to authenticate them. The authentication is based on keystroke dynamics which captures the user’s behavioural biometric and applies machine learning algorithms to classify them. To protect the users from illegitimate use of their accounts, the attributes of how they use their keyboard and mouse are taken into account for authorizing the user. This will contribute significantly to the field of cyber-security by forming a robust authentication system using machine learning algorithms. In this talk, concepts of feature extraction, feature selection, and classification will be discussed.

Bio:  Miss. Sowndarya Krishnamoorthy is currently a Masters student in the UWindsor’s School of Computer Science, under the supervision of Dr. Luis Rueda since September 2016. She received her Bachelor’s degree in Information Technology from Anna University, Chennai in 2014 with a university rank in the entire state of TamilNadu, India. She did her co-op as an ERP programmer at the University of Windsor for eight months. She had also worked as a System Engineer for more than two years in a CMM level 5 organization TATA Consultancy Services, Chennai. She is a member of Golden Key International Honour Society for her excellence in academics. Her research interests include Pattern Recognition, Biometrics, Machine Learning, and Cyber Security.

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