Spam Detection on URL Using Machine Learning Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.36948/ijfmr.2025.v07i04.50732
· OA: W4412432586
Spam URLs pose a significant threat to online security, leading to issues such as phishing, malware and loss of user trust. Detecting these malicious URLs is essential to safeguard users and prevent cyber attacks. A machine learning-based system has been developed to detect spam URLs by analysing their structure and features, such as domain names, URL length, special characters and patterns that may indicate obfuscation. Various machine learning algorithms, including Random Forest, Decision Trees and Support Vector Machines, are employed to classify URLs with high accuracy, targeting a detection rate of 95% or more. The system is scalable, real-time and can be integrated across platforms like email services, websites and social media to protect users from malicious links. This solution enhances online safety, reduces cyber threats and provides a reliable tool for identifying and filtering harmful URLs.
