Leveraging Advanced Machine Learning Techniques for Phishing Website Detection


Authors : Vaishali Bhargava; Sharvan Kumar Garg

Volume/Issue : Volume 8 - 2023, Issue 9 - September

Google Scholar : https://tinyurl.com/25f6xzne

Scribd : https://tinyurl.com/3pyp984p

DOI : https://doi.org/10.5281/zenodo.8437510

Abstract : Phishing attacks, which target users through fraudulent websites and emails to steal critical information, continue to pose a significant danger to internet security. Traditional ways of detecting phishing websites frequently fall behind the shifting tactics used by fraudsters. Machine learning approaches are being used as a powerful tool for improving phishing detection capabilities in this kind of context. The current study investigates a novel Machine Learning Model for Detecting Phishing Websites that employ advanced algorithms and feature selection methodologies.Through a rigorous experimental approach, the study evaluates their performance using key metrics including accuracy, precision, and recall.Conversely, when the data was split 50-50, Random Forest yielded better results.

Keywords : Phishing; Classification; Decision Tree; Machine Learning; Cyber Security.

Phishing attacks, which target users through fraudulent websites and emails to steal critical information, continue to pose a significant danger to internet security. Traditional ways of detecting phishing websites frequently fall behind the shifting tactics used by fraudsters. Machine learning approaches are being used as a powerful tool for improving phishing detection capabilities in this kind of context. The current study investigates a novel Machine Learning Model for Detecting Phishing Websites that employ advanced algorithms and feature selection methodologies.Through a rigorous experimental approach, the study evaluates their performance using key metrics including accuracy, precision, and recall.Conversely, when the data was split 50-50, Random Forest yielded better results.

Keywords : Phishing; Classification; Decision Tree; Machine Learning; Cyber Security.

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