DETECTION OF PHISHING ATTACKS USING DEEP LEARNING

Abstract

In the current digital landscape, several web sites cater to diverse objectives, including data
dissemination, advertising, social interaction, and more. However, there are certain online sites that engage in
criminal activities or phishing. Phishing refers to the act of gathering confidential information from someone,
including financial data and personal details. This activity has the potential to endanger customers. Phishing
Websites are online websites that engage in phishing operations. The identification of phishing sites is a well
studied field that is crucial for browsers, online apps, and other software. Several technologies, including data
mining and text mining, have been explored to identify phishing websites. Most prior research is centered on
text-based frameworks that utilize web page text data and employ text mining methods to evaluate phishing
data. The primary objective of this study was to investigate methods that rely on attributes to identify phishing
websites. This study used contemporary methodologies to incorporate Machine Learning and Deep Learning
algorithms into features-based methods. The primary objective of this study is to design an appropriate
categorization model for accurately predicting phishing websites.