README.md

metadata

license: mit
language:
  - en
tags:
  - zeek
  - network-anomaly-detection
  - ml
  - research

🚀 Try It Live (Google Colab Demo)

You can test the Truth-Zeeker AI model directly in Google Colab using the link below. This demo notebook automatically loads the model from Hugging Face and runs inference on a small pseudonymized Zeek dataset.

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📊 Demo Outputs

Sample visualization:
This chart shows the top anomalous hosts detected by Truth-Zeeker AI on a pseudonymized VLAN dataset (for demonstration only).

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🧠 Model and Data

• Model: model_20251020.joblib
• Demo CSV: zeek_features_for_training_pseudo.csv

These files are hosted on Hugging Face under the repository
dr-rakshith-truth-zeeker/truth-zeeker-ai-demo

Truth-Zeeker AI — Model Card (demo)

Overview

Small demonstration model for the Truth-Zeeker AI pipeline.
This repo contains a tiny synthetic dataset and a demo script that trains/loads a minimal model and shows predictions.

Intended use

Educational / research demo only. Not for production. Use only with sanitized or synthetic data.

Model details

• Algorithm (demo): IsolationForest (scikit-learn) for anomaly scoring
• Input features: duration, orig_bytes, resp_bytes
• Output: anomaly score / binary flag

Limitations

• Demo model is trained on synthetic data and is not validated on real traffic.
• Do not use with real PHI/PII or production network environments.

License

MIT