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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | A Review on Detection of Phishing Attacks using Machine Learning |
| Authors Name: | Ms. Kuchana Prathyusha , Mr. G. Shiva Prasad |
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IJRTI_189803
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| Published Paper Id: | IJRTI2405013 |
| Published In: | Volume 9 Issue 5, May-2024 |
| DOI: | |
| Abstract: | Web phishing attacks are one of the most critical web security concerns today. Still, phishers can gather substantial financial information about internet users and commit financial transgressions against them. Several conventional phishing website detection methods predict the appearance of phishing websites by examining blacklists. Phishing websites are a kind of social engineering effort that pretends to be conventional page and URL forgeries. The Link Guard algorithm is an idea created to detect phishing emails sent by attackers who want to host users and collect user information. It operates by carefully analyzing the characteristics of an email with phishing hyperlinks sent via email. Each user has a Link Guard algorithm, allowing him to perceive a phishing email or not and respond to it. The set of features of the algorithm provides the opportunity to detect and block known and novel phishing attacks effectively. It is possible to compile a list of Website URLs, including benign Websites and phish websites. This will allow me to generate the dataset and examine the necessary URLs and content-based features. This study presents an anti-web spoofing approach that targets the evaluation of the URL structure of brand-new web spoofing pages. Several measures were followed to evaluate the URL website attributes. The results show that the Random Forest classifier provides the best accuracy for the given dataset; accordingly, the accuracy rate of the classifier is measured based on the prediction readings at 96.82%. |
| Keywords: | Machine Learning, URLs, K-Nearest Neighbor, Link Guard, Kernel Support vector machine, decision tree. |
| Cite Article: | "A Review on Detection of Phishing Attacks using Machine Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 5, page no.78 - 82, May-2024, Available :http://www.ijrti.org/papers/IJRTI2405013.pdf |
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| 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 |
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Published Paper ID: IJRTI2405013
Registration ID:189803
Published In: Volume 9 Issue 5, May-2024
DOI (Digital Object Identifier):
Page No: 78 - 82 Country: Warangal, Telangana, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2405013 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2405013 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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