Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (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)
This project aims to develop an effective phishing URL detection system utilizing a combination of deep learning algorithms and feature engineering techniques. The system will scrutinize various URL characteristics, including domain name, path, length, and the presence of suspicious keywords, to accurately identify phishing URLs and promptly alert users prior to clicking on them. Evaluation of the system will be conducted using a comprehensive dataset comprising known phishing and legitimate URLs, with performance metrics such as precision, recall, accuracy, and F1 score being computed. Comparative analysis against existing phishing detection tools will be performed to assess the system's effectiveness and efficiency. Ultimately, this project endeavors to enhance the reliability and efficacy of phishing detection systems, thereby safeguarding users against falling victim to phishing scams and mitigating the risk of personal information theft.
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Cite Article:
"Advanced Phishing site Prediction Using Deep Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 4, page no.641 - 649, April-2024, Available :http://www.ijrti.org/papers/IJRTI2404090.pdf
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000205281
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator