witfoo/precinct6-cybersecurity-100m

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 removed through a 4-layer iterative pipeline:

1. Structured field sanitization + Aho-Corasick sweep (~166,000 patterns) with product identifier protection
2. Format-specific message parsing (8 parsers: Cisco ASA, Windows XML, WinLogBeat, CloudTrail, PAN, VMware, DNS, generic)
3. ML/NER residual detection (Presidio + BERT NER)
4. Claude AI contextual review

Final PII registry: ~302,000 unique mappings (IPs, hostnames, usernames, orgs, credentials, SIDs, emails, ARNs, etc.)

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}
}

License

Apache License 2.0