Visible to the public Keystroke Analysis for User Identification Using Deep Neural Networks

TitleKeystroke Analysis for User Identification Using Deep Neural Networks
Publication TypeConference Paper
Year of Publication2019
AuthorsBernardi, Mario Luca, Cimitile, Marta, Martinelli, Fabio, Mercaldo, Francesco
Conference Name2019 International Joint Conference on Neural Networks (IJCNN)
ISBN Number978-1-7281-1985-4
Keywordsartificial intelligence, authentication systems, Decision trees, decision-trees machine learning algorithm, Deep Learning, deep learning-based classifiers, deep neural network architecture, feature model, Human Behavior, human factors, keystroke, keystroke analysis, learning (artificial intelligence), malicious users, message authentication, Metrics, MLP deep neural network, multilayer perceptrons, neural net architecture, Neural Network, pattern classification, pubcrawl, supervised learning, typing dynamics, typing style, user identification

The current authentication systems based on password and pin code are not enough to guarantee attacks from malicious users. For this reason, in the last years, several studies are proposed with the aim to identify the users basing on their typing dynamics. In this paper, we propose a deep neural network architecture aimed to discriminate between different users using a set of keystroke features. The idea behind the proposed method is to identify the users silently and continuously during their typing on a monitored system. To perform such user identification effectively, we propose a feature model able to capture the typing style that is specific to each given user. The proposed approach is evaluated on a large dataset derived by integrating two real-world datasets from existing studies. The merged dataset contains a total of 1530 different users each writing a set of different typing samples. Several deep neural networks, with an increasing number of hidden layers and two different sets of features, are tested with the aim to find the best configuration. The final best classifier scores a precision equal to 0.997, a recall equal to 0.99 and an accuracy equal to 99% using an MLP deep neural network with 9 hidden layers. Finally, the performances obtained by using the deep learning approach are also compared with the performance of traditional decision-trees machine learning algorithm, attesting the effectiveness of the deep learning-based classifiers in the domain of keystroke analysis.

Citation Keybernardi_keystroke_2019