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

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.

detect phishing URLs to enhance online security and predict potential threats

fraudurl: fast, offline phishing URL detector and checker for Python. Check one URL or a whole CSV of links for phishing, with calibrated probabilities and plain-English reasons. One file, zero dependencies, never visits the sites.

Explainable phishing URL detection: 28 engineered features + gradient boosting + rule floors, typosquatting/homoglyph detection, CLI and FastAPI. F1 0.98, ROC-AUC 0.999.

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

an ML model to detect whether a website URL is legitimate or potentially phishing.

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.

Explainable URL-risk triage demo using classical machine learning and transparent lexical features.

A phishing-URL detector that judges a link from the string alone, plus the log of eight shortcuts the model took while I was building it. AUC 0.987 internal, 0.948 on an independently collected holdout.

Explainable phishing URL detection with calibrated ML (93.6% accuracy, 91.4 F1), SHAP analysis, threat intelligence, and a FastAPI + React forensic dashboard.

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

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.

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.