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 | Malicious URL Detection Using Machine Learning |
|---|---|
| ABSTRACT | Malicious URLs are among the most common vectors used to deliver phishing, malware, spam, and defacement attacks, affecting millions of internet users worldwide and causing significant financial and data loss. Traditional detection methods such as blacklisting and signature-based filtering are often reactive, unable to detect newly generated malicious URLs, and require frequent manual updates. To address these limitations, this paper presents a Malicious URL Detection framework that utilizes machine learning techniques to analyze URL-related parameters such as lexical features, host-based features, domain age, and page content characteristics. The collected data undergoes preprocessing and feature extraction to improve prediction accuracy. Multiple classification algorithms are employed to identify URL categories including benign, phishing, malware, and defacement URLs. The framework provides automated analysis and reliable predictions, reducing dependence on manual blacklist maintenance. Experimental evaluation demonstrates the effectiveness of the proposed approach in detecting malicious URLs with high accuracy. |
| AUTHOR | DR. T.V.S SRIRAM, SURESH GOPI, K. ASHOK 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 | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407027 |
| pdf/27_Malicious URL Detection Using Machine Learning.pdf | |
| KEYWORDS | |
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