ISSN2456-3315
Impact Factor10.57
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CrossRefDOI
ESTD2016
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AccessOpen
Submission: Always Open
Paper Title
Phishing URL Detection Using Machine Learning
Authors
Dr. Yamuna Devi N , Ashwitha C , Haridharani S P
Keywords
Machine Learning, Predictive Analytics, Classification Models, Phishing Detection, Logistic Regression, Linear Discriminant Analysis, K-means Clustering, Supervised Learning, Cyber Security, URL analysis, Fake websites, URL based classification, Threat Intelligence.
Abstract
Phishing is a cyber attack where users are misled into visiting fake websites that steal sensitive information. This study uses a machine learning based approach to detect phishing URLs through Logistic Regression and Linear Discriminant Analysis. A balanced dataset of 10,000 URLs (5,000 phishing and 5,000 legitimate) is used for training and testing. Principal Component Analysis (PCA) and Factor Analysis (FA) were applied for feature selection and both methods consistently identified thirteen key URL features. These features, along with K-Means clustering for risk grouping, helped achieve up to 88.3% accuracy using Logistic Regression. The method is efficient, scalable, and suitable for real time phishing detection.
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IJRTI — journal style
"Phishing URL Detection Using Machine Learning", IJRTI - International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.10, Issue 9, page no.a434-a440, September-2025, Available :https://ijrti.org/papers/IJRTI2509050.pdf
APA — 7th edition
N, Y. D., C, A., & P, H. S. (2025). Phishing URL Detection Using Machine Learning. International Journal for Research Trends and Innovation, 10(9), a434-a440. https://doi.org/10.56975/ijrti.v10i9.206016
MLA — 9th edition
N, Yamuna Devi, et al. "Phishing URL Detection Using Machine Learning." International Journal for Research Trends and Innovation, vol. 10, no. 9, 2025, pp. a434-a440, https://doi.org/10.56975/ijrti.v10i9.206016.
Chicago — 17th, bibliography
N, Yamuna Devi, Ashwitha C, and Haridharani S P. "Phishing URL Detection Using Machine Learning." International Journal for Research Trends and Innovation 10, no. 9 (2025): a434-a440. https://doi.org/10.56975/ijrti.v10i9.206016.
Harvard — author–date
N, Y.D., C, A. and P, H.S. (2025) 'Phishing URL Detection Using Machine Learning', International Journal for Research Trends and Innovation, 10(9), pp. a434-a440. Available at: https://doi.org/10.56975/ijrti.v10i9.206016
IEEE — numbered reference
Y. D. N, A. C and H. S. P, "Phishing URL Detection Using Machine Learning," IJRTI, vol. 10, no. 9, pp. a434-a440, Sep. 2025, doi: 10.56975/ijrti.v10i9.206016.
Vancouver — biomedical
N YD, C A, P HS. Phishing URL Detection Using Machine Learning. IJRTI. 2025 Sep;10(9):a434-a440. doi: 10.56975/ijrti.v10i9.206016
AMA — 11th edition
N YD, C A, P HS. Phishing URL Detection Using Machine Learning. IJRTI. 2025;10(9):a434-a440. doi:10.56975/ijrti.v10i9.206016
BibTeX — Zotero, Mendeley, LaTeX
@article{IJRTI2509050,
author = {Yamuna Devi N and Ashwitha C and Haridharani S P},
title = {Phishing URL Detection Using Machine Learning},
journal = {International Journal for Research Trends and Innovation},
volume = {10},
number = {9},
pages = {a434-a440},
year = {2025},
month = {September},
issn = {2456-3315},
doi = {10.56975/ijrti.v10i9.206016},
url = {https://doi.org/10.56975/ijrti.v10i9.206016}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - N, Yamuna Devi
AU - C, Ashwitha
AU - P, Haridharani S
TI - Phishing URL Detection Using Machine Learning
T2 - International Journal for Research Trends and Innovation
JA - IJRTI
VL - 10
IS - 9
PY - 2025
SN - 2456-3315
DO - 10.56975/ijrti.v10i9.206016
UR - https://doi.org/10.56975/ijrti.v10i9.206016
SP - a434-a440
ER -
Issue
Volume 10 Issue 9,
September-2025
Pages : a434-a440
Other Publication Details
Paper Reg. ID
IJRTI_206016
Published Paper ID
IJRTI2509050
Downloads
205,690
Research Area
Computer Science & TechnologyÂ
Country
Coimbatore, TamilNadu, India
Published
September 2025
Published Paper PDF
https://ijrti.org/papers/IJRTI2509050.pdf
Published Paper URL
https://ijrti.org/viewpaperforall.php?paper=IJRTI2509050
CrossRef DOI
https://doi.org/10.56975/ijrti.v10i9.206016
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About Publisher
International Journal for Research Trends and Innovation
Published by IJRTI (JW Publication)
2456-3315
ISSN
10.57
Impact Factor
2016
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Open
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Impact Factor 10.57 calculated by Google Scholar and Semantic Scholar.
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The content, data and findings in this article are based on the authors’ research and have been
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Declarations
Funding
No external funding was received for this study.
Conflict of Interest
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank the reviewers and the editorial board of International Journal for Research Trends and Innovation for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.
