Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{201056,
        author = {Mrs. R. Hemalatha and Jagan B and Muralidharan R and Ragupathi D and Sivakumar M},
        title = {AI-Powered Phishing Website Detection using NLP and Computer Vision},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {287-294},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201056},
        abstract = {Phishing websites are malicious web pages designed to deceive users into providing sensitive information such as passwords, credit card numbers, and personal details. Traditional phishing detection systems rely mainly on blacklist-based or URL-based detection methods, which fail to detect newly generated phishing websites.
This project proposes an AI-powered phishing website detection system that combines Natural Language Processing (NLP) and Computer Vision (CV) techniques. NLP analyzes textual content, URLs, and metadata, while Computer Vision examines webpage screenshots to detect visual similarities with legitimate websites.
By integrating both text-based and visual-based detection, the system improves detection accuracy and reduces false positives. The proposed model aims to provide real-time phishing detection for enhanced cybersecurity.},
        keywords = {Artificial Intelligence (AI), Natural Language Processing (NLP), Computer Vision (CV), Phishing Website Detection, Machine Learning, Deep Learning, URL Analysis, Convolutional Neural Networks (CNN), Cybersecurity, Real-Time Detection.},
        month = {May},
        }