  In the growing world of the internet, the number of ways to obtain crucial
data such as passwords and login credentials, as well as sensitive personal
information has expanded. Page impersonation, often known as phishing, is one
method of obtaining such valuable information. Phishing is one of the most
straightforward forms of cyberattack for hackers and one of the simplest for
victims to fall for. It can also provide hackers with everything they need to
get access to their target's personal and corporate accounts. Such websites do
not offer a service, but instead, gather personal information from users. In
this paper, we achieved state-of-the-art accuracy in detecting malicious URLs
using recurrent neural networks. Unlike previous studies, which looked at
online content, URLs, and traffic numbers, we merely look at the text in the
URL, which makes it quicker and catches zero-day assaults. The network has been
optimised to be utilised on tiny devices like Mobiles, and Raspberry Pi without
sacrificing the inference time.
