A Survey on Phishing URL Detection

Ananya E Bhat , Dr. Kavyasri M N , Arpitha H R , Sharath N , Karthik Pai

Phishing Detection, Malicious URL, Chrome Extension, Random Forest, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Real- Time Detection.

Phishing and malicious URLs have become an extensive concern for cybersecurity focusing on individuals, businesses, and government entities. Machine learning has therefore been identified as a robust solution for identifying automatically and precisely these types of threats. This literature review summarizes results from five recent papers that investigate several machine learning and deep learning methods, such as CNNs, LSTMs, Random Forests, Gradient Boosting, k-NN, Logistic Regression, and SVMs. They highlight feature engineering, binary visualization, real-time detection systems, and hybrid schemes in achieving high detection accuracy. They also accentuate the disadvantage of conventional blacklisting techniques and underpin the use of URL lexical features, domain-based heuristics, and image analysis-based techniques for their effectiveness in defeating dynamic phishing tactics. The collective findings attempt to give an overall picture of the present state of affairs in malicious URL detection research and influence future developments within this vital area.

Published Paper   E-Certificate

"A Survey on Phishing URL Detection", IJNTI - INTERNATIONAL JOURNAL OF NOVEL TRENDS AND INNOVATION (www.IJNTI.org), ISSN:2984-908X, Vol.3, Issue 5, page no.a248-a251, May-2025, Available :https://rjpn.org/IJNTI/papers/IJNTI2505028.pdf

Volume 3 Issue 5, May-2025

Pages : a248-a251

Paper Reg. ID: IJNTI_231300

Published Paper Id: IJNTI2505028

Downloads: 0001025

Research Area: Science and Technology

Country: Hassan, Karnataka, India

Published Paper PDF: https://rjpn.org/IJNTI/papers/IJNTI2505028

Published Paper URL: https://rjpn.org/IJNTI/viewpaperforall?paper=IJNTI2505028

DOI: https://doi.org/10.56975/ijnti.v3i5.231300

ISSN: 2984-908X | IMPACT FACTOR: 9.57 Calculated By Google Scholar | ESTD YEAR: 2023

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: RJPN (IJPublication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition