Dense deep neural network architecture for keystroke dynamics authentication in mobile phone

Abstract

The ever-growing technology in mobile smartphones has enabled users to store sensitive and private information; as a result, it required the need for an improved security system. Previous approaches heavily relied on shallow machine learning algorithms that require feature extraction which is time consuming, laborious and can cause, resulting in poor authentication. In this paper, we propose a deep learning - dense neural network to avoid the limitation of the classical algorithms and build a mobile smartphone touch screen authentication scheme based on keystroke dynamics. A deep learning - dense neural network classifier was trained using keystroke dynamics features extracted from users. A comparative analysis was made between our proposed DNN classifier and some selected classical machine learning algorithms on the keystroke dynamics data. The data is split into five different data partition of training and testing. Results clearly indicated that the deep learning - dense neural network has eliminated the feature extraction steps required by the classical algorithms and improved the overall authentication accuracy, as such, improved the security of the smartphone device. In addition, it is found that the propose deep learning - dense neural network authentication scheme is more robust than the classical algorithms and has the potential to be fully implemented on smartphone to improve the security system of the mobile smartphone touch screen devices.