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

Phishing attacks are considered one of the highest risks to cybersecurity, as they exploit users by pretending to be a valid site and gaining access to sensitive data. Traditional methods of detection, including blacklist-based systems, cannot uncover newly created phishing sites (zero-day attacks), need regular updates to their databases, and can generally not offer real-time protection. The paper suggests implementing a hybrid phishing site detection system involving machine learning with rule-based analysis to improve the accuracy and reliability of detection. 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. Three supervised machine learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression, where RF was indicated to achieve the highest performance. Moreover, a rule-based system of brand impersonation detection is also implemented to make the system resistant to targeted phishing attacks. Experimental findings prove that the hybrid methodology enhances classification accuracy and lowers false negatives compared to conventional ones. The proposed hybrid model represents an accuracy of 96%, with a recall of 94% and F1 of 95% ; it can be deployed in real-time and gives interpretable results by using confidence scoring. The solution suggested is a feasible and effective method of phishing detection and can be expanded to more sophisticated intelligent cybersecurity systems.

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.