Phishing Detection
The DNA test for websites
A web-based OSINT tool that provides URL analysis, Exif data extraction, and reverse phone lookup.
CrawlerBox is an automated analysis framework designed for parsing emails and crawling embedded web resources.
A strict iOS app that analyzes link safety like a nutrition label (no AI, offline)
WhoDAT is an InfoSec Analyzer for Nerds using VirusTotal, Google Safe Browsing, URLScan, Hybrid-Analysis, and OpenAI. Scan URLs, emails, headers, and attachments (including QR codes) for malicious activity!
Offline phishing detection model for websites using a hybrid CNN–LSTM architecture. Operates without internet access, classifying URLs as legitimate or potentially malicious based on learned patterns.
Python-based tool for analyzing URLs and detecting potential threats using various cybersecurity services.
Phishing URL detector using ML & feature engineering. End-to-end system with API, Docker, and CI/CD. Python • scikit-learn • BeautifulSoup.
OpenSiteTrust is an open, explainable, and reusable website scoring ecosystem
CLI tool to search in URLhaus database and submit URLs
detect phishing URLs to enhance online security and predict potential threats
AI powered phishing detection system using multimodal analysis (text, URLs, images). Combines BERT, DistilBERT & ResNet50 with 99.5% accuracy. Built with FastAPI + React.
Automate risk assessments for any URL using the public Zulu Zscaler web service. CLI tool for single, manual lookups – ideal for security research, education, and integration.
An analyzer which uses web services to scan files and URL.
PHANTOM is an AI-powered phishing detection system that analyzes emails, messages, and URLs using NLP, machine learning, and cyber-threat intelligence to identify scams in real time.
Android application which uses openlysis back end.
🔍 Detect phishing websites offline using a combined CNN and LSTM model, analyzing URL features for high accuracy in classification.
🔒 Machine learning-powered phishing URL detection system built with Flask and scikit-learn. Analyzes 30+ website features to identify malicious URLs with real-time safety scoring.
Suspicious URL scanner using Python. Detects risky keywords, IP domains, shortening services, and more. Great for OSINT and cybersecurity learning.
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