Phishing Website Detection

Abstract

Phishing websites have emerged as a serious security risk. Phishing is the starting point for many cyberattacks that compromise the confidentiality, integrity, and availability of customer and business data. Decades of effort have gone into developing novel methods for automatically identifying phishing websites. Modern systems aren't very adept at spotting new phishing threats and require a lot of manual feature engineering, even though they can produce better outcomes. Thus, an open problem in this discipline is to identify tactics that can swiftly handle zero-day phishing attempts and automatically recognize phishing websites. The web page that the URL hosts has a plethora of information that can be utilized to assess the maliciousness of the web server. One useful technique for spotting phishing emails is machine learning. Additionally, it does away with the drawbacks of the earlier approach. After a careful analysis of the literature, we proposed a novel approach that combines a machine learning algorithm with feature extraction to identify phishing websites. Using the gathered dataset, this study aims to train deep neural networks and machine learning models to detect phishing websites.