Paper Key : IRJ************219
Author: Jaya Tripathi,Mahak Srivastava,Radhey Shyam,Ajay Srivastava
Date Published: 03 May 2024
Abstract
With advancements in technology, with each passing day, a large number of users are joining and surfing the web. Along with this, there has also been a rise in cybercrimes. Phishing website creation is one of the most prevalent one which traps innocent users by making them to access or click on the URLs (Uniform Resource Locators). Detecting phishing websites is vital in the fight against online misinformation and fraud. This research proposes a comprehensive approach, utilizing machine learning (ML) technique, to identify fraudulent websites. By scrutinizing a range of features, including content, design, and user interactions, our method employs ML algorithms to categorize websites as genuine or fake. We introduce a multi-stage detection framework that integrates feature extraction, classification, and validation steps to improve accuracy. Moreover, we explore the integration of external data sources such as social media signals to enhance detection performance. Through extensive experimentation across various datasets, our approach demonstrates both robustness and effectiveness in discerning between authentic and fake websites. This study contributes to the advancement of techniques aimed at combating online deception and protecting users from malicious online entities.
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