Ensemble Guard: A Focused Machine Learning Approach for Detecting Harmful URLs in Modern Cybersecurity
Year: 2025
Type: article
Abstract: Ensemble Guard is a URL Threat Detector is a specialized cybersecurity solution engineered to detect and assess potentially harmful web links. It utilizes an advanced detection framework integrating multiple machine learning models, heuristic methods, and behavioral analysis. This ensemble-based strategy significantly enhances the accuracy and responsiveness of threat identification. The system inspects various URL components-including its structure, domain credibility, and contextual cues-to evaluate its potential for misuse. By concentrating solely on URLs, Ensemble Guard achieves exceptional precision in identifying threats linked to phishing, malware, and online fraud. Designed for versatility, it can be easily integrated into web browsers, email systems, and network security tools.
Authors: Ahalya A, Shyma Kareem
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409600454