• Dhruvi Patel
• Dhaval Chudasama

Phishing attacks are one of the greatest threats to online security, where fraud websites deceive users into giving out sensitive information. Traditional methods of detection, such as blacklists and heuristic-based systems, often fail in identifying newly created or sophisticated phishing websites. This study proposes an intelligent phishing website detection system using Convolutional Neural Networks (CNNs) in analyzing URLs and associated features. Using labeled URLs, the system employs such attributes such as URL length, domain age, HTTPS usage, and redirection patterns to train a CNN model.The potential applications include browser extensions, email filters, and cybersecurity tools. The proposed approach improves user protection against phishing attacks and mitigates the risks associated with it. This research underlines the efficiency of machine learning in dealing with dynamic and evolving cybersecurity challenges.

Phishing Detection, URL Analysis, Machine Learning, Deep Learning, Feature Extraction, Decision Trees, Real-Time Detection, Convolutional Neural Networks (CNNs)

"Detection of phishing website using URL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.m99-m106, April-2025, Available :http://www.jetir.org/papers/JETIR2504C11.pdf

"Detection of phishing website using URL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppm99-m106, April-2025, Available at : http://www.jetir.org/papers/JETIR2504C11.pdf