README.md

---
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
configs:
- config_name: default
  data_files:
  - split: meta
    path: data/meta-*
dataset_info:
  features:
  - name: section
    dtype: string
  - name: name
    dtype: string
  - name: description
    dtype: string
  - name: payload_json
    dtype: string
  - name: version
    dtype: string
  - name: created_at
    dtype: string
  splits:
  - name: meta
    num_bytes: 2518
    num_examples: 6
  download_size: 5613
  dataset_size: 2518
---

# 🔒 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)

---

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