Abstract:
Account recovery is a critical aspect of web application security, often overlooked
despite its importance. Traditional account recovery methods, such as sending a
password reset link or a new username to the user’s registered email, are vulnerable
to impostors who may have access to the user’s email and other credentials.
This vulnerability makes account recovery a potential weak point in the overall security
of a web application. Recent applications of behavioral biometrics, such as
keystroke dynamics, for attack detection and user authentication bear similarities
to biometric authentication. Adding keystroke dynamics analysis to the account
recovery process significantly increases the difficulty for an impostor to successfully
recover and take over a user’s account. To enhance user authentication effectiveness
and raise account recovery requirements through keystroke dynamics, this
study adds one additional measure of keystroke patterns to the already-existing
features. Compared to other access control systems based on biometric features
like face or fingerprint, keystroke analysis has attained a respectable level of accuracy.
In this aim, this study uses experimental data and statistical analysis to
show how the unique keystroke measure provided may be utilized in conjunction
with the current authentication mechanism to greatly improve the authentication
and security of sensitive applications. It may be beneficial to recognize the intruders
and expel them from the system as long as this job can accommodate their
typing rhythm. In this study, generative adversarial networks (GAN) are utilized
to generate keyboard dynamics data with a focus on impersonating a user at the
identification step in both fixed text and fixed sentence contexts. Three distinct
architectures have been devised, implemented, and validated with the aid of machine
learning and deep learning: vanilla-GAN based on simple neural networks
NN, LSTM-GAN based on recurrent neural networks using long short-term memories
(LSTM), CNN-GAN based on convolutional neural networks. The developed
Conditional Generative Adversarial Networks have shown that these architectures
can successfully replicate a user’s keystroke dynamics by learning about the user’s
typing style and generating keyboard dynamics data using different GANs with
different architectural styles. Findings show that keystroke dynamics patterns can
be efficiently produced by the GAN and utilized to trick keystroke authentication
systems.
