Datasets:

witfoo
/

precinct6-cybersecurity-100m

@@ -1,208 +0,0 @@
----
-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 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
-```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)