LAUSR

The uniqueness of social interactions on online social networks draws attention to cybersecurity research. Social Behavioral Biometric (SBB) systems extract unique patterns from online communication traits trails and generate digital fingerprints for user identification. However, with time those behavioral patterns change. These affect the authentication ability of a SBB system. In this paper, we have combined for the first time textual, contextual and interpersonal communicative information of users in online social networks to develop a biometric system. The SBB traits are combined using the weighted sum rule score level fusion algorithm with the genetic algorithm employed to choose the feature weights. The effects of template aging on the individual SBB traits and overall system have been analyzed for the first time. The proposed system achieves the recognition accuracy of 99.25% and outperforms all prior research on SBB. The experimental results on permanence evaluation demonstrate that the developed system can perform remarkably well despite the template aging effect.