Abstract: Various authentication methods have been proposed to mitigate data breaches. However, the increasing frequency of data breaches and users' lack of awareness have exposed traditional methods, including single-factor password-based systems and, two-factor authentication systems, to vulnerabilities against attacks. While behavioral authentication holds promise in tackling these issues, it faces challenges concerning interoperability between operating systems, the security of behavioral data, accuracy enhancement, scalability, and cost. This research presents a scalable dynamic behavioral authentication model utilizing keystroke typing patterns. The model is constructed around five key components: human-computer interface devices, encryption of behavioral data, consideration of the authenticator's emotional state, incorporation of cross-platform features, and proposed implementation solutions. It addresses potential typing errors and employs data encryption for behavioral data, achieving a harmonious blend of usability and security by leveraging keyboard dynamics. This is accomplished through the implementation of a web-based authentication system that integrates Convolutional Neural Networks (CNNs) for advanced feature engineering. Keystroke typing patterns were gathered from participants and subsequently employed to evaluate the system's keystroke timing verification, login ID verification, and error handling capabilities. The web-based system uniquely identifies users by merging their username-password (UN-PW) credentials with their keyboard typing patterns, all while securely storing the keystroke data. Given the achievement of a 100% accuracy rate, the proposed Behavioral Dynamics Authentication Model (BDA) introduces future researchers to five scalable constructs. These constructs offer an optimal combination, tailored to the device and context, for maximizing effectiveness. This achievement underscores its potential applications in the realm of authentication.

Idowu Dauda Oladipo, Mathew Nicho, Joseph Bamidele Awotunde, Jemima Omotola Buari, Muyideen Abdulraheem and Tarek Gaber, “A Secure and Scalable Behavioral Dynamics Authentication Model” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140804

@article{Oladipo2023,
title = {A Secure and Scalable Behavioral Dynamics Authentication Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140804},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140804},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Idowu Dauda Oladipo and Mathew Nicho and Joseph Bamidele Awotunde and Jemima Omotola Buari and Muyideen Abdulraheem and Tarek Gaber}
}