INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Phishing attacks pose a significant threat, deceiving users into surrendering sensitive information. This study investigates the effectiveness of machine learning in detecting phishing URLs. We train and evaluate various models using a comprehensive dataset encompassing URL structure, website content, and external information. Exploratory data analysis identifies key features, and feature engineering further enhances model capabilities. The Gradient Boosting Classifier achieves a remarkable 97.4% accuracy in identifying phishing attempts. Analysis reveals that HTTPS presence, URL anchor text, and website traffic patterns significantly influence the model's decisions. We acknowledge the need for regular model updates due to evolving phishing tactics and emphasize the importance of user education as a complementary defense strategy. Future research avenues include exploring external data sources, investigating ensemble models, and continuously monitoring phishing trends for improved detection methods.
"Phishing URL Detection Using Gradient Boosting: A Machine Learning Approach", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.f652-f660, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403574.pdf
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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