Authors - Priyal Donda, Vatsal Upadhyay, Janhavi Gulabani, Sharvari Patil, Vinaya Sawant Abstract - Phishing is increasingly being one of the frequent cyber-attacks. Since this trend has seen the incidence increased significantly in the last few years, people and organizations have been highly affected by data breaches and financial losses. Such growth only increases the demand for effective mechanisms of defense, as traditional approaches of machine learning like SVM, Random Forest, and Long Short-Term Memory networks often fail to detect phishing attempts with accuracy. SVMs can be computationally expensive, sensitive to noise, and require careful selection of kernel functions, while LSTMs are complex, prone to overfitting, and require substantial amounts of labeled data. In light of these limitations, the use of GANs has been recent in order to improve detection capabilities. GANs create realistic phishing URLs that advanced detection models struggle to distinguish, using semi-supervised training to differentiate between adversarial and legitimate URLs. Specifically, this holistic approach grapples with the sophistication of phishing attacks and places an emphasis on adaptive defense, since it has changed the basis for detection from content-based to URL-based techniques. Finally, these novel approaches introduce a promising pathway for the mitigation of phishing risks and sensitive information safeguarding, thus building security strength in the digital world.
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