Explainable Phishing Website Detection Using Comparative Machine Learning and SHAP

Phishing Website Detection Using A Stacked Hybrid Model With Explainable AI

A Phishing Website Detection System Using a Stacked Hybrid Model With Explainable AI, designed to accurately classify websites as phishing or legitimate while providing transparent decision explanations, is presented.

Phishing Website Detection Using Machine Learning

Abstract— Phishing website detection using machine learning focuses on the design and implementation of an intelligent system for detecting malicious URLs using machine learning techniques. The system aims to classify URLs as either legitimate or malicious by analyzing various structural and statistical features extracted from the URLs. A dataset containing both benign and malicious URLs is used to train and evaluate the model. The proposed approach utilizes a Gradient Boosting Classifier due to its high accuracy and ability to handle complex patterns in data. Feature extraction plays a crucial role in the project, where attributes such as URL length, presence of special characters, domain age, use of HTTPS, and abnormal patterns are considered. These features are fed into the model, which learns to differentiate between safe and harmful URLs. The project involves several stages, including data collection, preprocessing, feature extraction, model training, and performance evaluation. Multiple machine learning algorithms such as Support Vector Machine (SVM), Decision Tree, Random Forest, and XG Boost are also explored and compared to identify the most effective model .The system is designed to work in real time, allowing users to input URLs and receive instant predictions regarding their safety. Keywords— Phishing Website Detection, Machine Learning, Gradient Boosting Classifier, URL Feature Extraction, Cybersecurity, Malicious URL Detection, Web Security, Classification, Feature Engineering, Real-Time Detection.

Phishing Website Detection using Artificial Neural Networks and Count Vectorization: A Web-Deployed Approach

Phishing URL Detection Using TF-IDF Character N-Gram and Complement Naive Bayes

Efficiency makes the proposed approach exceptionally suitable for real-time detection in resource-constrained environments, such as mobile applications or browser extensions, providing an accessible and proactive layer of defense for end-users.

A Hybrid Machine Learning and Rule-Based Approach for Phishing Website Detection Using URL Features

The model proposed employs feature extraction using URLs, such as lexical and structural features like URL length, frequency of special characters, use of IP addresses, and occurrence of suspicious keywords, to improve the accuracy and reliability of detection.

An Explainable Hybrid Model for Real-Time Phishing Detection Using Multi-Source Features

External validation against ten official Iraqi university websites resulted in correct classification with no false positives, demonstrating that the proposed phishing detection model is reliable.