Active Behavioural Biometric Authentication Using Cat Swarm Optimization Variants With Deep Learning

Abstract

Security issues have only been compounded by the advent of distributed networks and global internet availability. Combating these security issues depends on being able to correctly authenticate a valid user. This paper presents variants of our CRNM framework which is an efficient cat swarm optimized deep learning model to accurately authenticate a valid user through signature behavioral patterns and biometric information of the user. The behavioral patterns considered here are keystroke and mouse dynamics. Face recognition has been included as a means to decrease the false rejection rate of the system. The major contributions of this work include comparison of various cat optimization variants for active authentication, performance analysis of our model with different state of the art systems. The fitness functions tested include Rosenbrock, Rastrigin and Griewank while CSO variants studied are ADCSO, AICSO and PCSO. Results of our experiments indicate that the proposed authentication system is faster and more efficient than existing frameworks. We achieve accuracy 98.29%, FAR 0.01 and FRR 1.02.