Duplicate from witfoo/precinct6-cybersecurity-100m

Co-authored-by: Charles Herring <f15hb0wn@users.noreply.huggingface.co>

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README.md

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+---
+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)

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