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

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

Machine Learning model for URL Reputation dataset from UCI ML Repository

URL classification by Naive Bayes algorithm

multi-label url classification

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

Proactive Malicious URL Detection: ML Defense 🌐🛡️"

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

detect phishing URLs to enhance online security and predict potential threats

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

FastAPI implementation of URL shortener system with ML-powered malicious URL classification.

Hybrid phishing URL detector using a character-level CNN + handcrafted URL features in PyTorch, with Streamlit-based interactive inference.

This project implements a Machine Learning and Deep Learning hybrid approach to detect phishing websites. By analyzing URLs and their associated features, the system predicts whether a given website is legitimate or phishing, leveraging multiple ML algorithms and neural networks for improved accuracy.

An API for URL classification using XGBoost. Identifies whether a URL is benign or malicious based on lexical and host-based features.

A machine-learning–based phishing URL detector built using 87 handcrafted lexical, content-based, and external features. Includes full feature extraction, model training, evaluation, and a Flask API for real-time URL classification.

A scalable machine learning framework for malicious website detection, featuring modular data preprocessing and multi-model classification support.一个可扩展的恶意网站识别机器学习框架，支持模块化数据预处理与多模型分类检测。

Machine learning system for detecting malicious URLs using Random Forest and Logistic Regression. Features REST API, live dashboard, and Docker deployment.