Abstract:
Tabnabbing attacks exploit user behavior in
web browsers, deceiving users by altering content in inactive
tabs to appear legitimate, leading to data disclosure or
unintended actions. This research evaluates the effectiveness
of Reinforcement Learning (RL) in detecting Tabnabbing
attacks at the web browser level, presenting a proactive
defense mechanism against this cyber threat. The study began
with a literature review to find the top 5 critical features of
Tabnabbing attacks and were extracted using a publicly
available dataset from "Phishpedia". Data preprocessing is
conducted to handle missing and incorrect data, resulting in
a refined dataset. The RL agent is designed using the Deep QNetwork
(DQN) algorithm, which effectively handles highdimensional
state spaces. The evaluation of the RL agent
demonstrates promising results. However, there is room for
improvement requiring further research and model tuning.
