Online User Recognition using Social Behavioral Biometric System
Date
2022-04
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Abstract
Online Social Networking (OSN) platforms have become an integral part of the daily life of individuals around the world. The uniqueness of social interactions on online social networks draws the attention of cybersecurity research. Social Behavioral Biometric (SBB) systems extract unique patterns from online communication trails and generate digital fingerprints for user identification. This research investigates the impact of users’ vocabulary to conclude whether such features contribute to user authentication. This thesis combines for the first time the textual, contextual and interpersonal communicative information of users in online social networks to develop a social behavioral biometric system. This thesis also pioneers the comparative study of fusion methods in SBB systems. The proposed system achieves 99.25% recognition accuracy and outperforms all prior research on SBB. In addition, the effects of template aging on the individual SBB traits and the overall system have been analyzed first-ever. The experimental results on permanence evaluation demonstrate that the developed system can perform remarkably well despite the template aging effect.
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Keywords
Social Behavioral Biometrics, Online Social Network, User Identification, Cyber Security
Citation
Tumpa, S. N. (2022). Online user recognition using social behavioral biometric system (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.