An Intelligent Scheme for Continuous Authentication of Smartphone Using Deep Auto Encoder and Softmax Regression Model Easy for User Brain

Abstract

The smartphones are becoming more popular these days because of having more technologies in a smaller and slim-sized device. The people can get Internet connectivity and some other facilities, such as communication and storing data, with these smaller devices. It is necessary for securing personal data on smartphone devices with authentication techniques. In recent years, most of the static authentication techniques, such as password, patterns, and fingerprint, were used for securing smartphones. Most of the studies are focusing on a new method called continuous authentication for improving the security of smartphones. The continuous authentication techniques differ from other static authentication techniques by authenticating the smartphone user periodically. It uses behavioral features of users for authenticating the user. Continuous authentication is easy for the user's brain, as a user need not to memorize anything. In this paper, a continuous authentication technique is designed with the Deep Auto Encoder and Softmax Regression techniques (DAE-SR). The DAE-SR achieved 0.950% and 0.970% of accuracy on predicting the users on different states (walking and sitting) of users.