✉️ PhishEye — AI‑ready Phishing Email Analyzer (MVP)

PhishEye parses .eml emails, extracts links/domains, applies phishing heuristics, assigns a risk score, and exports a JSON report you can visualize in a local dashboard.

Start with rule/regex based detection. Add NLP/ML later for even more power.

🚀 Features

• Parse .eml files (single or folder)
• Extract links/domains from body (HTML + text)
• Heuristics: suspicious TLDs, IP‑based URLs, punycode, brand impersonation, bait keywords
• Risk score → Low / Medium / High
• Export to output/phisheye_report.json
• Local dashboard in web/index.html (drop JSON to visualize)

📦 Quick Start

python main.py -i samples/email1.eml
# or analyze a folder
python main.py -i samples/

Then open web/index.html and upload output/phisheye_report.json.

🧪 Example

Sample email provided in samples/email1.eml (impersonation + suspicious TLD + IP link).

🧠 Extend (next steps)

• Add domain age check via APIs
• NLP classification (scikit‑learn / spaCy / transformers)
• HTML display-name vs href mismatch checks
• Export PDF summaries

📂 Structure

PhishEye/
  main.py
  modules/
    parser.py
    detector.py
    report.py
  samples/
    email1.eml
  web/
    index.html
  output/
  README.md
  requirements.txt

Educational use only. Do not use with private data you’re not authorized to process.