  Phishing is the simplest form of cybercrime with the objective of baiting
people into giving away delicate information such as individually recognizable
data, banking and credit card details, or even credentials and passwords. This
type of simple yet most effective cyber-attack is usually launched through
emails, phone calls, or instant messages. The credential or private data stolen
are then used to get access to critical records of the victims and can result
in extensive fraud and monetary loss. Hence, sending malicious messages to
victims is a stepping stone of the phishing procedure. A \textit{phisher}
usually setups a deceptive website, where the victims are conned into entering
credentials and sensitive information. It is therefore important to detect
these types of malicious websites before causing any harmful damages to
victims. Inspired by the evolving nature of the phishing websites, this paper
introduces a novel approach based on deep reinforcement learning to model and
detect malicious URLs. The proposed model is capable of adapting to the dynamic
behavior of the phishing websites and thus learn the features associated with
phishing website detection.
