PhishStats

About PhishStats

PhishStats is a phishing intelligence database and real-time feed, operating since 2014. It provides verified phishing URLs, IPs, domains, and threat scores through a web search interface and a JSON REST API, with search filterable by URL, IP, country, ASN, and date range, plus data visualization, mapping, and export capabilities. The database updates every 90 minutes and includes a phishscore, a confidence rating calculated from 22 features using machine learning to assess how likely a URL is a phishing site.

The platform is built for SOC analysts, threat hunters, security teams, and academic researchers conducting phishing research and incident response. Users can submit phishing URLs and report false positives, with submissions scored by a hybrid rule-based and machine-learning engine and reviewed by an AI system. Monitoring and webhook alerts for new phishing URLs are available, integrating with Slack, Microsoft Teams, Discord, Google Chat, and Telegram. Record detail pages support pivoting on title, IP, domain, body hash, tracking IDs, and redirect chains to identify shared infrastructure and correlated campaigns.

API access is tiered: anonymous requests are capped at 50 per day per IP, free registered users get 150 per day with an API key, Premium plans allow 1,000 per day, and Enterprise plans allow 10,000 per day, with all quotas resetting at UTC midnight. Read-only access to the search interface and API requires no signup, while paid Premium and Enterprise plans offer higher API volume, submission limits, and monitoring limits, billed monthly or annually.

Key features

• Search millions of verified phishing URLs, IPs, domains and threat scores
• Real-time REST API with JSON output for SIEM/SOAR integration
• Database updated every 90 minutes with new phishing intelligence
• Monitoring and webhooks for Slack, Microsoft Teams, Discord, Google Chat and Telegram
• Submit phishing URLs and report false positives via web interface or API
• Phishscore confidence rating calculated from 22 features using machine learning

Frequently asked questions