CNN-BASED CONTINUOUS AUTHENTICATION OF SMARTPHONES USING MOBILE SENSORS

Abstract

With the advancement of technology, the smartphone has become a reliable source to store private details, personal photos, credentials, and confidential information. However, smartphones are easily stolen and it has become a suitable target for attackers. Usually, smartphones require only initial explicit authentication, once the initial login is passed all the information can be accessed easily. Hence, this paper proposes an efficient implicit, continuous authentication of the smartphone based on the user’s behavioral characteristics. We propose architecture to differentiate legitimate smartphone owners from intruders. Our model relies on the smartphone’s built-in sensors like an accelerometer, gyroscope, and GPS. The sensors respond according to the user’s behavior which is recorded by the smartphone. We have used the rest filter model to separate motion data from rest since the rest data does not contain much information about the user’s behavior. We have used Xgboost and Convolutional Neural Network as our rest filter and legitimate-intruder classifier respectively. Our system can predict legitimate and intruders in a few seconds. Our proposed CNN model has an achieved average accuracy of 95.79% in our custom dataset, which has further improved after integrating GPS data.