Spam Detection on URL Using Machine Learning

Author(s)	Dr. Krishna Anand, Mr. S Abhiram, Mr. Gokul SP, Mr. Prem Kumar R, Mr. Bharath S
Country	India
Abstract	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.
Keywords	URL, Spam Detection, Random Forest, Feature Extraction, CNN
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-07-13
DOI	https://doi.org/10.36948/ijfmr.2025.v07i04.50732
Short DOI	https://doi.org/g9s9px

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International Journal For Multidisciplinary Research