  Spear Phishing is a type of cyber-attack where the attacker sends hyperlinks
through email on well-researched targets. The objective is to obtain sensitive
information by imitating oneself as a trustworthy website. In recent times,
deep learning has become the standard for defending against such attacks.
However, these architectures were designed with only defense in mind. Moreover,
the attacker's perspective and motivation are absent while creating such
models. To address this, we need a game-theoretic approach to understand the
perspective of the attacker (Hacker) and the defender (Phishing URL detector).
We propose a Conditional Generative Adversarial Network with novel training
strategy for real-time phishing URL detection. Additionally, we train our
architecture in a semi-supervised manner to distinguish between adversarial and
real examples, along with detecting malicious and benign URLs. We also design
two games between the attacker and defender in training and deployment settings
by utilizing the game-theoretic perspective. Our experiments confirm that the
proposed architecture surpasses recent state-of-the-art architectures for
phishing URLs detection.
