Improved Generative Adversarial Network for Phishing Attack Detection
L Shammi, C. Emilin Shyni · 2024
A general form of attack that is happening over the internet is called phishing which could lead to identity theft and financial damages. Due to the increase of online electronic services and payment systems, the demand for accurate phishing detection tools has risen in recent times. However, the models often lead to high false detection rates. This research work introduces an Improved Generative Adversarial Network-based phishing attack detection, which has mainly two stages such as preprocessing and attack detection. Initially, for data preprocessing, a min-max normalization process is used. Following that, the attack detection process is done via Improved GAN, where a new discriminator loss function is adopted to enhance the detection performance. Finally, the performance of the proposed work is validated in terms of different performance measures.