International Journal of Leading Research Publication

E-ISSN: 2582-8010   •   Impact Factor: 9.56

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

Call for Paper Volume 6 Issue 11 November 2025 Submit your research before last 3 days of to publish your research paper in the issue of November.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 11
November 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

AI-BASED PHISHING WEBSITE DETECTION

Author(s) Ravi Kumar Mahone, Nishanshu Deshmukh, Nishant Sahu, Sahil Darokar, Sangam jain
Country India
Abstract Phishing remains one of the most persistent and detrimental cyber threats in the modern digital landscape. Traditional defence mechanisms, such as blacklists and manual reporting, are critically ineffective against the rapid evolution and deployment of sophisticated, zero-day phishing sites. This review paper explores the transition to Artificial Intelligence (AI) and Machine Learning (ML) as a robust, adaptive solution to this problem. We outline a systematic methodology involving feature engineering—analysing URL characteristics, domain metadata, and page content—followed by the application of classification algorithms such as Decision Trees, Random Forests, and Support Vector Machines (SVM). Through critical analysis, we compare the performance trade-offs between simple classifiers and ensemble methods, highlighting the superior accuracy and generalisation capability of ensemble models like Random Forest. The paper provides an extensive survey of existing literature, tracing the evolution from simple ML techniques to state-of-the-art Deep Learning and hybrid systems. Finally, we identify key research gaps, notably model vulnerability to adversarial attacks and the need for standardised, real-time deployment strategies, outlining the future scope for developing continuous learning and hybrid detection mechanisms.
Field Engineering
Published In Volume 6, Issue 11, November 2025
Published On 2025-11-14
Cite This AI-BASED PHISHING WEBSITE DETECTION - Ravi Kumar Mahone, Nishanshu Deshmukh, Nishant Sahu, Sahil Darokar, Sangam jain - IJLRP Volume 6, Issue 11, November 2025. DOI 10.70528/IJLRP.v6.i11.1828
DOI https://doi.org/10.70528/IJLRP.v6.i11.1828
Short DOI https://doi.org/hbb4jg
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJLRP DOI prefix is
10.70528/IJLRP

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 10 Cover Page Vol 6 Isu 9 Cover Page Vol 6 Isu 8

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJLRP
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijlrp.com