Phishing Website Detection using Neural Networks

Phishing is a pervasive technique used by cybercriminals to deceive individuals into divulging personal information by using counterfeit websites designed to look like legitimate ones. These phishing websites are crafted to steal sensitive data such as usernames, passwords, and financial details by mimicking the appearance and language of authentic sites, making it challenging for users to discern the difference. As phishing techniques rapidly evolve alongside technological advancements, employing effective anti-phishing strategies .is crucial. Machine learning emerges as a powerful tool in counteracting phishing attacks, as attackers find it easier to trick victims into clicking seemingly genuine malicious links than to penetrate computer security systems directly. These links often feature the spoofed company’s logos and authentic information, adding to their credibility. Our proposed method leverages machine learning, specifically the Gradient Boosting Classifier, to create an innovative approach for detecting phishing websites by analyzing URL features. This involves evaluating characteristics such as URL length, suspicious characters, and uncommon domain extensions, which help differentiate legitimate sites from phishing attempts. Our approach operates in real time, allowing for the swift identification and mitigation of threats. The results of our studies show that this method effectively distinguishes between legitimate and fraudulent websites, offering a reliable means of real-time protection against phishing attacks. This innovative solution not only enhances security but also provides a scalable approach to addressing the ever-evolving nature of phishing threats.