README.md

---
license: apache-2.0
task_categories:
  - text-classification
  - graph-ml
language:
  - en
tags:
  - cybersecurity
  - intrusion-detection
  - provenance-graphs
  - MITRE-ATT&CK
  - SOAR
  - security-operations
  - IDS
  - network-security
  - threat-detection
  - labeled-dataset
  - lead-rules
size_categories:
  - 100M<n<1B
dataset_info:
  - config_name: signals
    splits:
      - name: train
        num_examples: 114074530
configs:
  - config_name: signals
    data_files:
      - split: train
        path: signals/*.parquet
  - config_name: graph_nodes
    data_files:
      - split: train
        path: graph/nodes.jsonl
  - config_name: graph_edges
    data_files:
      - split: train
        path: graph/edges.jsonl
  - config_name: incidents
    data_files:
      - split: train
        path: graph/incidents.jsonl
---

# WitFoo Precinct6 Cybersecurity Dataset (114M)

## Overview

A large-scale, labeled cybersecurity dataset derived from production Security Operations Center (SOC) data processed by [WitFoo Precinct](https://www.witfoo.com/) version 6.x. This dataset contains **114 million sanitized security events** (signal logs) and **provenance graphs** (10,442 incident graphs with 23,362 nodes and 32,732,650 edges) from real enterprise network monitoring across 5 organizations.

**Available in two sizes:**
- [`witfoo/precinct6-cybersecurity`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity) — 2M signals (smaller, faster to load)
- [`witfoo/precinct6-cybersecurity-100m`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity-100m) — **114M signals (this dataset)**

**Generate your own:** WitFoo Precinct 6.x customers can create datasets from their own data using the open-source pipeline: [`witfoo/dataset-from-precinct6`](https://github.com/witfoo/dataset-from-precinct6)

This dataset is designed to support research in:
- **Provenance graph-based intrusion detection** (KnowHow, NodLink, and similar systems)
- **AI-driven cyber defense simulation** (CybORG and MARL-based defense policy training)
- **Security alert classification** (malicious vs. suspicious vs. benign event labeling)
- **Attack lifecycle analysis** using MITRE ATT&CK framework mappings
- **Detection rule evaluation** using WitFoo's 261 lead detection rules

## Quick Start

```python
from datasets import load_dataset

# Load flat signal logs (114M rows across 58 Parquet shards)
signals = load_dataset("witfoo/precinct6-cybersecurity-100m", "signals", split="train")

# Find malicious events (from confirmed incidents)
malicious = signals.filter(lambda x: x["label_binary"] == "malicious")

# Find suspicious events (matched detection rules)
suspicious = signals.filter(lambda x: x["label_binary"] == "suspicious")

# Query by product/vendor
cisco_events = signals.filter(lambda x: x["vendor_name"] == "Cisco")

# Load provenance graph
nodes = load_dataset("witfoo/precinct6-cybersecurity-100m", "graph_nodes", split="train")
edges = load_dataset("witfoo/precinct6-cybersecurity-100m", "graph_edges", split="train")

# Load full incident graphs
incidents = load_dataset("witfoo/precinct6-cybersecurity-100m", "incidents", split="train")
```

## Label Distribution

| Label | Count | Percentage |
|-------|-------|------------|
| `benign` | 113,326,050 | 99.34% |
| `malicious` | 125,780 | 0.11% |
| `suspicious` | 622,700 | 0.55% |

## Signal Columns

| Column | Type | Description |
|--------|------|-------------|
| `timestamp` | float | Unix epoch timestamp |
| `message_type` | string | Event classification (e.g., `firewall_action`, `account_logon`, `AssumeRole`) |
| `stream_name` | string | Source product/data stream (see Source Products below) |
| `pipeline` | string | Ingestion pipeline |
| `src_ip` | string | Source IP (sanitized) |
| `dst_ip` | string | Destination IP (sanitized) |
| `src_port` | string | Source port |
| `dst_port` | string | Destination port |
| `protocol` | string | Network protocol (6=TCP, 17=UDP, 1=ICMP) |
| `src_host` | string | Source hostname (sanitized) |
| `dst_host` | string | Destination hostname (sanitized) |
| `username` | string | Associated username (sanitized) |
| `action` | string | Event action (block, permit, logon, logoff) |
| `severity` | string | Severity level |
| `vendor_code` | string | Vendor-specific event code |
| `message_sanitized` | string | Full sanitized raw log message |
| `label_binary` | string | `malicious`, `suspicious`, or `benign` |
| `label_confidence` | float | Confidence score (0.0–1.0) |
| `suspicion_score` | float | WitFoo suspicion score (0.0–1.0) |
| `mo_name` | string | Modus operandi (e.g., `Data Theft`) |
| `lifecycle_stage` | string | Kill chain stage (e.g., `initial-compromise`, `complete-mission`) |
| `matched_rules` | string | JSON array of matched WitFoo lead rule descriptions |
| `set_roles` | string | JSON array of classification roles (e.g., `Exploiting Host`, `C2 Server`) |
| `product_name` | string | Security product name (e.g., `ASA Firewall`, `Falcon`) |
| `vendor_name` | string | Product vendor (e.g., `Cisco`, `Crowdstrike`) |

## Source Products

The dataset contains events from **158 security products** across **70+ vendors**. Complete catalog in `reference/lead_rules_catalog.json`.

| Category | Products |
|----------|----------|
| **Firewalls** | Cisco ASA, Palo Alto PAN NGFW, Fortinet FortiGate, Checkpoint, Meraki, SonicWall, pfSense, Barracuda, Juniper SRX |
| **Endpoint Protection** | CrowdStrike Falcon, Symantec SEP, Carbon Black, Cylance, SentinelOne, Deep Instinct, Sophos, McAfee, ESET |
| **Network Detection** | Cisco Stealthwatch, Cisco Firepower, Suricata IDS, TippingPoint IPS, Vectra Cognito |
| **Identity & Access** | Microsoft Windows AD, Cisco ISE, Centrify, CyberArk, Duo, Okta, Beyond Trust |
| **Cloud Security** | AWS CloudTrail, AWS VPC Flow Logs, AWS GuardDuty, Azure Security, Zscaler, Netskope, Cisco Umbrella |
| **Email Security** | ProofPoint, Mimecast, FireEye EX, Barracuda ESS, Cisco IronPort, Checkpoint Harmony |
| **Threat Intelligence** | FireEye NX/HX/AX/CMS, Trend Micro, QRadar, Microsoft ATA, Cortex XDR |
| **Infrastructure** | VMware vCenter/NSX, Elastic Filebeat, Linux (sshd, PAM, systemd, auditd), Apache, HAProxy |

## Labeling Methodology

**Three-tier labels** derived from two sources:

- **`malicious`** (125,780): Events embedded as leads inside confirmed incidents. Extracted directly from incident lead objects with suspicion scores, modus operandi, and MITRE mappings.
- **`suspicious`** (622,700): Events matching WitFoo's 261 lead detection rules (e.g., "ASA Deny", "Windows Failed Login", "CrowdStrike Detection") but not in confirmed incidents.
- **`benign`** (113,326,050): Events not matching any detection rules and not in any incident.

The `matched_rules` column shows which detection rules matched. The `set_roles` column shows WitFoo classification roles (Exploiting Host, C2 Server, Staging Target, etc.).

## Lead Detection Rules

261 rules included in `reference/lead_rules_catalog.json`. Examples:

| Rule | Criteria | Roles |
|------|----------|-------|
| Blocked Action | Any firewall block | Exploiting Host → Exploiting Target |
| ASA Deny | cisco_asa + deny | Exploiting Host → Exploiting Target |
| Windows Failed Login | Event ID 4625 | Exploiting Target → Exploiting Host |
| CrowdStrike Detection | CrowdStrike stream | Exploiting Target → Exploiting Host |
| AWS VPC Reject | VPC flow + REJECT | Exploiting Host → Exploiting Target |
| Authentication Failure | auth_failure type | Exploiting Host → Exploiting Target |
| Audit log cleared | Event ID 1102 | Exploiting Target → Exploiting Host |

## Sanitization

All customer-identifying information has been removed through a comprehensive, iterative four-layer sanitization pipeline. Quality was prioritized over processing speed. The pipeline is [open source](https://github.com/witfoo/dataset-from-precinct6) under the Apache 2.0 license.

1. **Structured field sanitization + Aho-Corasick multi-pattern sweep** — Known fields are replaced with deterministic tokens (IPs to [RFC 5737](https://datatracker.ietf.org/doc/html/rfc5737) ranges, hostnames to `HOST-NNNN`, etc.). Every record is then swept using an [Aho-Corasick automaton](https://en.wikipedia.org/wiki/Aho%E2%80%93Corasick_algorithm) built from all 302,000+ registry entries to catch PII in unexpected contexts. Product identifiers are explicitly protected from the sweep.
2. **Format-specific log message parsing** — Eight specialized parsers handle Cisco ASA syslog, Windows Security Event XML, WinLogBeat JSON, AWS CloudTrail, Palo Alto Networks, VMware vCenter, DNS logs, and a generic fallback — sanitizing PII within structured fields like XML elements, nested JSON, and CSV columns.
3. **Machine learning residual detection** — [Microsoft Presidio](https://microsoft.github.io/presidio/) (spaCy NLP) and [BERT NER](https://huggingface.co/dslim/bert-base-NER) scan sanitized records for residual PII that pattern-based approaches miss. New discoveries trigger full re-sanitization.
4. **Large language model contextual review** — [Claude](https://www.anthropic.com/claude) reviews stratified samples for subtle PII: embedded org names, internal hostnames revealing organizational structure, employee names in file paths, AD group names, and geographic identifiers. Findings trigger re-sanitization.

**Iterative convergence:** The four layers run in cycles. PII discovered by ML/AI in one cycle is caught automatically by Layer 1 in all subsequent cycles across the complete dataset. Cycles repeat until near-zero new discoveries.

**Final PII registry:** ~302,000 unique mappings across 14 categories (IPs, hostnames, usernames, orgs, credentials, SIDs, emails, ARNs, etc.). All replacements are consistent — the same original value always maps to the same token, preserving graph topology.

## Graph Data

| Component | Count |
|-----------|-------|
| Nodes | 23,362 |
| Edges | 32,732,650 |
| Incidents | 10,442 (Data Theft: 10,441, Phishing: 1) |

## Limitations

- **Label imbalance**: 99.77% benign reflects production SOC reality. Sampling strategies needed for balanced training.
- **Temporal scope**: July–August 2024
- **Sanitization**: ~302,000 PII registry entries used for consistent replacement across all records
- **Shared incidents**: Same 10,442 incidents appear in both 2M and 114M datasets
- **Sanitization trade-offs**: Some log message detail reduced by PII replacement

## Citation

```bibtex
@dataset{witfoo_precinct6_100m_2025,
  title={WitFoo Precinct6 Cybersecurity Dataset (114M)},
  author={WitFoo, Inc.},
  year={2025},
  url={https://huggingface.co/datasets/witfoo/precinct6-cybersecurity-100m},
  license={Apache-2.0}
}
```

## License

[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)