Integrating deep learning and data fusion for advanced keystroke dynamics authentication

Abstract

By enhancing user authentication protocols, especially in critical infrastructures vulnerable to complex cyberthreats, we present an advanced approach that integrates a deep learning-based model and data fusion techniques applied to analyze keystroke dynamics. With the growing need for robust security measures, especially in critical infrastructure environments, traditional authentication mechanisms often fail to cope with advanced threats. Our approach focuses on the unique behavioral biometric characteristics of keystrokes, which offers promising opportunities to improve user authentication processes. We have developed a data fusion-based methodology that utilizes the unique features of keystroke dynamics combined with deep learning techniques to improve user authentication systems. Using the capabilities of data fusion and deep learning, the proposed methodology not only captures the complex behavioral biometrics inherent in keystroke dynamics but also addresses the challenges posed by varying password lengths and typing styles. We conducted extensive experiments on several fixed-text datasets, including the Carnegie Mellon University dataset, the KeyRecs dataset, and the GREYC-NISLAB dataset, with a total of approximately 54,000 password records. Comprehensive experiments on various datasets with different password lengths have shown that our approach is scalable and accurate for user authentication, which significantly improves the security of critical infrastructure. By using interpolation-based data fusion techniques to standardize the keystroke data to a uniform length and employing a Siamese neural network with a triplet loss function, the best equal error rate of 0.13281 was achieved for the unseen fused data. The integration of deep learning and data fusion effectively generalizes to different user profiles, demonstrating its adaptability and accuracy in authenticating users in different scenarios. The findings are crucial for improving security in sensitive applications, ranging from accessing personal devices to protecting critical infrastructure.