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 | URL-Based Phishing Detection |
|---|---|
| ABSTRACT | Phishing attacks have become one of the most prevalent cybersecurity threats, targeting users through fraudulent websites designed to steal sensitive information such as login credentials, banking details, and personal data. Traditional blacklist-based detection methods are often ineffective against newly created phishing websites, highlighting the need for intelligent and automated detection mechanisms. The proposed system extracts discriminative features such as URL length, the presence of special characters, the number of subdomains, HTTPS usage, domain-related attributes, and suspicious keywords. These features are used to train and evaluate multiple supervised machine learning algorithms, including Random Forest, Decision Tree, Support Vector Machine, and Logistic Regression. The proposed model provides fast, scalable, and reliable phishing detection, making it suitable for integration into web browsers, email filtering systems, and enterprise cybersecurity solutions. This research contributes to strengthening online security by providing an efficient and accurate phishing detection framework capable of identifying both known and previously unseen phishing URLs. |
| AUTHOR | DR. T.V.S SRIRAM, S. JAYAPRADHA, G. BALA JYOTHI Sr. Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.14060103 |
| pdf/103_URL-Based Phishing Detection.pdf | |
| KEYWORDS | |
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