International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Phishing Website Detection using Deep Learning |
|---|---|
| ABSTRACT | Phishing attacks are a major cybersecurity threat, targeting users to steal sensitive information such as login credentials, financial data, and personal identity details. Traditional detection methods based on blacklists, heuristics, or rule-based systems are often insufficient to handle the increasing sophistication and variability of phishing websites. This paper proposes a deep learning-based framework for phishing website detection that leverages both URL-based and HTML-based features. The model utilizes a Convolutional Neural Network (CNN) to automatically extract meaningful patterns from input features and classify websites as phishing or legitimate. A dataset of 10,000 websites, evenly balanced between phishing and legitimate samples, is used to train and evaluate the model. Experimental results demonstrate that the proposed approach achieves high detection accuracy, precision, recall, and F1-score, outperforming traditional machine learning methods. The study highlights the potential of deep learning techniques in providing robust, scalable, and real-time phishing detection, which can significantly enhance cybersecurity measures. |
| AUTHOR | SOHAM DABHOLKAR, SHIVRAJ CHORAGE, SUYASH SHINDE, VEDANT AKHADE, ANUPAMA. B |
| PUBLICATION DATE | 2025-10-30 |
| VOLUME | 175 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1310026 |
| pdf/26_Phishing Website Detection.pdf | |
| KEYWORDS |
