Real Time Malicious URL Detection Using Machine Learning

Nijanthan N; Abhimanyu C; Amalraj M.O; Karthik Sankkar V.B

doi:10.36948/ijfmr.2025.v07i06.60234

Real Time Malicious URL Detection Using Machine Learning

Author(s)	Mr Nijanthan N, Mr. Abhimanyu C, Mr. Amalraj M.O, Mr. Karthik Sankkar V.B
Country	India
Abstract	The increasing dependence on digital communication and online transactions has introduced a range of cybersecurity challenges. Among these, the use of malicious Uniform Resource Locators (URLs) has emerged as one of the most deceptive and damaging attack vectors. Attackers craft URLs that appear legitimate but are designed to deliver malware, execute phishing schemes, or steal sensitive user information. Traditional blacklist-based or rule-based detection mechanisms have become increasingly ineffective in the face of rapidly evolving, obfuscated, and zero-day malicious URLs. To address these limitations, this study proposes a real-time malicious URL detection system that integrates traditional machine learning algorithms with advanced deep learning models such as the Bidirectional Encoder Representations from Transformers (BERT). The proposed hybrid model leverages both lexical and contextual URL features to achieve higher detection accuracy and adaptability. It combines algorithms like Support Vector Machine (SVM) and Random Forest with BERT’s natural language understanding capabilities to analyse and classify URLs efficiently. The system is designed for deployment in real-world environments such as browsers, email filters, and enterprise-level security frameworks. Performance evaluation metrics such as accuracy, precision, recall, and F1-score are used to validate the effectiveness of the model. Additionally, explainable AI (XAI) methods are incorporated to ensure transparency, allowing analysts to understand the reasoning behind model decisions. The results demonstrate that the hybrid approach significantly outperforms existing methods in terms of real-time adaptability and detection accuracy.
Keywords	Malicious URL Detection, Cybersecurity, Machine Learning, BERT, Explainable AI, Real-Time Threat Analysis
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-13
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.60234

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Real Time Malicious URL Detection Using Machine Learning

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