PhishNet: A Phishing Website Detection Tool using XGBoost
Prashant Kumar, Kevin Antony, Deepakmoney Banga, Arshpreet Sohal

TL;DR
PhisNet is an AI-powered web application that detects phishing websites using machine learning algorithms and feature extraction, providing real-time predictions to enhance cybersecurity defenses.
Contribution
The paper introduces PhisNet, a novel phishing detection tool that combines multiple machine learning models with a user-friendly web interface for improved accuracy and usability.
Findings
High detection accuracy achieved with optimized models
Effective feature extraction improves model performance
Real-time prediction capability demonstrated
Abstract
PhisNet is a cutting-edge web application designed to detect phishing websites using advanced machine learning. It aims to help individuals and organizations identify and prevent phishing attacks through a robust AI framework. PhisNet utilizes Python to apply various machine learning algorithms and feature extraction techniques for high accuracy and efficiency. The project starts by collecting and preprocessing a comprehensive dataset of URLs, comprising both phishing and legitimate sites. Key features such as URL length, special characters, and domain age are extracted to effectively train the model. Multiple machine learning algorithms, including logistic regression, decision trees, and neural networks, are evaluated to determine the best performance in phishing detection. The model is finely tuned to optimize metrics like accuracy, precision, recall, and the F1 score, ensuring…
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Taxonomy
TopicsSpam and Phishing Detection · Misinformation and Its Impacts · Advanced Malware Detection Techniques
