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.

Machine Learning model for URL Reputation dataset from UCI ML Repository

Defensive ML project for phishing URL and email risk classification using URL features, typosquatting detection, NLP, XGBoost, and Streamlit.

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.

🛡️ Real-time malicious URL detector — LightGBM + FastAPI + Chrome Extension. Catches phishing, malware and spam URLs before you click.

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。

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

BFS-based web crawler that classifies URLs using Regex, evaluates SEO metadata, and generates structured data quality reports.

k-Nearest Neighbours (scikit-learn), for PhiUSIIL phishing URL classification. 500,002 stored values, served over HTTP.

Machine Learning based phishing URL detection system built using Flask, TF-IDF, Scikit-learn and SQLite.

ML phishing URL classifier using feature extraction and ensemble methods. Trained on labeled datasets of malicious and legitimate URLs.

A machine learning project to detect phishing URLs using feature extraction and classification models.

Gaussian Naive Bayes (scikit-learn), for PhiUSIIL phishing URL classification. 202 parameters, served over HTTP.

GUI-based phishing URL detector using 7 ML/DL models (Logistic Regression, Random Forest, XGBoost, SVM, Naive Bayes, CNN, BiLSTM) with ensemble learning, built with Python & Tkinter.

k-Nearest Neighbours implemented from scratch, for PhiUSIIL phishing URL classification. 500,001 stored values in a NumPy .npz, no pickle, served over HTTP.

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

Gaussian Naive Bayes implemented from scratch, for PhiUSIIL phishing URL classification. 198 parameters in plain JSON, no pickle, served over HTTP.