url-classification

URI-URL Classification using Recurrent Neural Network, Support Vector and RandomForest. The Implementation results follows with classification report, confusion matrix and precision_recall_fscore_support for each validation result of a 10-fold crossval

Machine learning-based malicious URL detection system for classifying URLs as safe or harmful using feature extraction and predictive modeling.

To identify and extract features from URL that help classify URLs into benign/phishing and train an ML model with these features for classification.

QuickCharNet is a deep learning project that leverages an efficient character-level Convolutional Neural Network (CNN) for URL classification, aimed at enhancing Search Engine Optimization (SEO). The project includes datasets, model evaluation notebooks, and visualization scripts. Key features include data preprocessing, detailed model architecture

This repo is the dataset for the paper "A New Dataset and Methodology for Malicious URL Classification"

URL classification by Naive Bayes algorithm

Machine Learning model for URL Reputation dataset from UCI ML Repository

multi-label url classification

Proactive Malicious URL Detection: ML Defense 🌐🛡️"

Identifying Suspicious URLs: An Application of Large-Scale Online Learning (Python Reproducibility Experiment)

Safeguard-Chrome-Extension is an AI-powered, machine learning–based security tool that proactively detects and blocks malicious URLs in real time. Tested on 1000+ URLs with a high accuracy rate, it offers advanced threat detection, phishing prevention, and malware protection, ensuring secure, reliable browsing with optimized performance.

detect phishing URLs to enhance online security and predict potential threats

Safeguard-Web-Protector is an AI-driven, machine learning–powered Chrome extension that proactively detects and blocks malicious URLs in real time. It delivers intelligent threat analysis, phishing prevention, and malware protection, ensuring secure browsing. Optimized for high performance and accuracy, it enhances browsing resilience.

Modern, explainable phishing URL detection with FastAPI, policy bands, and LLM-based review. Fast, auditable, and easy to run locally or in Docker.

🛡️ A machine learning web app that detects phishing URLs in real-time using Random Forest Classifier — built with Python, Flask & Scikit-Learn.

A Node.js demonstration application showcasing full-text search capabilities using Elasticlunr.js for URL pattern matching. This project demonstrates how to efficiently match URLs against a set of rules with wildcard support. Built in November 2018. A practical example of implementing a full-text search engine for URL classification and matching.

PhishGuard v2, a phishing URL detection API trained on roughly 81k labelled URLs. Sentence-transformer embeddings into a Keras classifier, with strict URL validation, a health endpoint that admits when the model failed to load, and 17 tests.

🛡️🔍A collaborative phishing URL detection system featuring four complementary approaches: Classic ML (XGBoost/LightGBM), CNN+Ensemble, CNN+BiLSTM, and Hierarchical Multi-View Deep Learning. Achieves 98%+ accuracy with integrated Chrome extension for real-time protection.