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README.md

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+---
+license: mit
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- security
+- rl
+- network-security
+- anomaly-detection
+- verifiers
+- metadata-only
+pretty_name: "Security Verifiers E1 - Network Log Anomaly Detection (Metadata)"
+size_categories:
+- n<1K
+---
+
+# 🔒 Security Verifiers E1: Network Log Anomaly Detection (Public Metadata)
+
+> **⚠️ This is a PUBLIC metadata-only repository.** The full datasets are hosted privately to prevent training contamination. See below for access instructions.
+
+## Overview
+
+E1 is a network log anomaly detection environment with calibrated classification and abstention. This repository contains **only the sampling metadata** that describes how the private datasets were constructed.
+
+### Why Private Datasets?
+
+**Training contamination** is a critical concern for benchmark integrity. If datasets leak into public training corpora:
+- Models can memorize answers instead of learning to reason
+- Evaluation metrics become unreliable
+- Research reproducibility suffers
+- True capabilities become obscured
+
+By keeping evaluation datasets private with gated access, we:
+- ✅ Preserve benchmark validity over time
+- ✅ Enable fair model comparisons
+- ✅ Maintain research integrity
+- ✅ Allow controlled access for legitimate research
+
+### Dataset Composition
+
+The private E1 datasets include:
+
+#### Primary Dataset: IoT-23
+- **Samples**: 1,800 network flows (train/dev/test splits)
+- **Source**: IoT-23 botnet dataset
+- **Features**: Network flow statistics, timestamps, protocols
+- **Labels**: Benign vs Malicious with confidence scores
+- **Sampling**: Stratified by label and split
+
+#### Out-of-Distribution Datasets
+- **CIC-IDS-2017**: 600 samples (different attack patterns)
+- **UNSW-NB15**: 600 samples (different network environment)
+- **Purpose**: Test generalization and OOD detection
+
+### What's in This Repository?
+
+This public repository contains:
+
+1. **Sampling Metadata** (`sampling-*.json`):
+   - Dataset versions and sources
+   - Sampling strategies and random seeds
+   - Label distributions
+   - Split ratios
+   - Reproducibility parameters
+
+2. **Tools Versions** (referenced in metadata):
+   - Exact versions of all preprocessing tools
+   - Dataset library versions
+   - Python environment specifications
+
+3. **This README**: Instructions for requesting access
+
+### Reward Components
+
+E1 uses composable reward functions:
+- **Accuracy**: Correctness of malicious/benign classification
+- **Calibration**: Alignment between confidence and actual accuracy
+- **Abstention**: Reward for declining on uncertain examples
+- **Asymmetric Costs**: Higher penalty for false negatives (security context)
+
+### Requesting Access
+
+🔑 **To access the full private datasets:**
+
+1. **Open an access request issue**: [Security Verifiers Issues](https://github.com/intertwine/security-verifiers/issues)
+2. **Use the title**: "Dataset Access Request: E1"
+3. **Include**:
+   - Your name and affiliation
+   - Research purpose / use case
+   - HuggingFace username
+   - Commitment to not redistribute or publish the raw data
+
+**Approval criteria:**
+- Legitimate research or educational use
+- Understanding of contamination concerns
+- Agreement to usage terms
+
+We typically respond within 2-3 business days.
+
+### Citation
+
+If you use this environment or metadata in your research:
+
+```bibtex
+@misc{security-verifiers-2025,
+  title={Open Security Verifiers: Composable RL Environments for AI Safety},
+  author={intertwine},
+  year={2025},
+  url={https://github.com/intertwine/security-verifiers},
+  note={E1: Network Log Anomaly Detection}
+}
+```
+
+### Related Resources
+
+- **GitHub Repository**: [intertwine/security-verifiers](https://github.com/intertwine/security-verifiers)
+- **Documentation**: See `EXECUTIVE_SUMMARY.md` and `PRD.md` in the repo
+- **Framework**: Built on [Prime Intellect Verifiers](https://github.com/PrimeIntellect-ai/verifiers)
+- **Other Environments**: E2 (Config Verification), E3-E6 (in development)
+
+### License
+
+MIT License - See repository for full terms.
+
+### Contact
+
+- **Issues**: [GitHub Issues](https://github.com/intertwine/security-verifiers/issues)
+- **Discussions**: [GitHub Discussions](https://github.com/intertwine/security-verifiers/discussions)
+
+---
+
+**Built with ❤️ for the AI safety research community**