metadata
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 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— 2M signals (smaller, faster to load)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
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
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 under the Apache 2.0 license.
- Structured field sanitization + Aho-Corasick multi-pattern sweep — Known fields are replaced with deterministic tokens (IPs to RFC 5737 ranges, hostnames to
HOST-NNNN, etc.). Every record is then swept using an Aho-Corasick automaton built from all 302,000+ registry entries to catch PII in unexpected contexts. Product identifiers are explicitly protected from the sweep. - 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.
- Machine learning residual detection — Microsoft Presidio (spaCy NLP) and BERT NER scan sanitized records for residual PII that pattern-based approaches miss. New discoveries trigger full re-sanitization.
- Large language model contextual review — 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
@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}
}