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Replace dataset with 41.6M-pipeline output (UC fixes) — phase 1

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Phase 1 of UC-feedback regeneration:
- New README with full ground-truth, scoring, and MITRE documentation
- Delete stale signal shards (21 new shards uploaded in phase 2)
- Delete placeholder graph.graphml + graph.json (replaced by per-incident GraphMLs)
- New nodes.jsonl (44,717 nodes), attack_reports.jsonl (12,361 reports)

Files changed (3) hide show
  1. README.md +23 -23
  2. graph/nodes.jsonl +0 -0
  3. signals/metadata.json +87 -87
README.md CHANGED
@@ -18,12 +18,12 @@ tags:
18
  - labeled-dataset
19
  - lead-rules
20
  size_categories:
21
- - 10M<n<100M
22
  dataset_info:
23
  - config_name: signals
24
  splits:
25
  - name: train
26
- num_examples: 41658748
27
  configs:
28
  - config_name: signals
29
  data_files:
@@ -47,11 +47,11 @@ configs:
47
 
48
  ## Overview
49
 
50
- 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 **41,658,748 sanitized security events** (signal logs) across 5 organizations and **12,361 incident provenance graphs** (44,717 nodes, 9,494,858 edges).
51
 
52
  **Available in two sizes:**
53
  - [`witfoo/precinct6-cybersecurity`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity) — 2.1M signals (smaller, faster to load; same incidents and graph methodology)
54
- - [`witfoo/precinct6-cybersecurity-100m`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity-100m) — **41.6M signals (this dataset)**
55
 
56
  **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)
57
 
@@ -67,7 +67,7 @@ This dataset is designed to support research in:
67
  ```python
68
  from datasets import load_dataset
69
 
70
- # Load flat signal logs (41.6M rows across 21 Parquet shards)
71
  signals = load_dataset("witfoo/precinct6-cybersecurity-100m", "signals", split="train")
72
 
73
  # Find analyst-confirmed malicious events
@@ -78,7 +78,7 @@ malicious_confirmed = signals.filter(
78
  # Find suspicious events that matched detection rules but are not in confirmed incidents
79
  suspicious = signals.filter(lambda x: x["label_binary"] == "suspicious")
80
 
81
- # Load provenance graph (44.7k nodes, 9.5M edges)
82
  nodes = load_dataset("witfoo/precinct6-cybersecurity-100m", "graph_nodes", split="train")
83
  edges = load_dataset("witfoo/precinct6-cybersecurity-100m", "graph_edges", split="train")
84
 
@@ -90,17 +90,17 @@ incidents = load_dataset("witfoo/precinct6-cybersecurity-100m", "incidents", spl
90
 
91
  | Label | Count | Percentage |
92
  |-------|-------|------------|
93
- | `benign` | 41,447,721 | 99.49% |
94
- | `malicious` | 148,128 | 0.36% |
95
- | `suspicious` | 62,899 | 0.15% |
96
 
97
  Disposition breakdown (the raw Precinct status, exposed for ground-truth stratification — see [Ground Truth](#ground-truth-and-disposition)):
98
 
99
  | Disposition | Count | Meaning |
100
  |-------------|-------|---------|
101
- | `Unprocessed` | 41,575,946 | No analyst review (default state for benign/suspicious + un-reviewed malicious) |
102
- | `Disrupted` | 82,782 | SOC analyst confirmed and intervened |
103
- | `Dismissed` | 20 | SOC analyst dismissed |
104
 
105
  ## Signal Columns
106
 
@@ -159,9 +159,9 @@ The dataset contains events from **158 security products** across **70+ vendors*
159
 
160
  **Three-tier labels** derived from two sources:
161
 
162
- - **`malicious`** (148,128): Events embedded as leads inside confirmed incidents. Extracted directly from incident lead objects with suspicion scores, modus operandi, and MITRE mappings.
163
- - **`suspicious`** (62,899): Events matching WitFoo's 261 lead detection rules but not present in confirmed incidents.
164
- - **`benign`** (41,447,721): Events not matching any detection rules and not in any incident.
165
 
166
  ### Ground Truth and Disposition
167
 
@@ -176,7 +176,7 @@ The dataset contains events from **158 security products** across **70+ vendors*
176
  | `False Positive` | SOC analyst confirmed false positive | Negative — analyst rejected |
177
  | `Unprocessed` | Automated detection, no human review | Lower — Precinct-confidence only |
178
 
179
- The `disposition_category` column buckets these into four values for easier filtering: `confirmed-malicious`, `false-positive`, `dismissed`, `automated`. For experiments where ground-truth quality matters, restrict to records where `disposition` ∈ {`Disrupted`, `Resolved`} to compare against analyst-confirmed labels. **This dataset has 82,782 records with `Disrupted` status, 20 with `Dismissed`, and the remainder with `Unprocessed` (no analyst review).**
180
 
181
  For benign and suspicious records, `disposition` is `Unprocessed` (no incident association). For malicious records, `disposition` reflects the parent incident's status at extraction time.
182
 
@@ -212,8 +212,8 @@ Tactic IDs use the standard MITRE ATT&CK Enterprise format (`TA0001` through `TA
212
 
213
  | Component | Count |
214
  |-----------|-------|
215
- | Nodes (artifact-derived hosts + credentials) | 44,717 |
216
- | Edges (artifact `EVENT`/`NETWORK_FLOW` + incident `INCIDENT_LINK`) | 9,494,858 |
217
  | Incidents | 12,361 |
218
 
219
  Per-edge labels in `edges.jsonl` include `attack_techniques`, `attack_tactics`, `set_roles`, `lifecycle_stage`, `disposition`, `mo_name`, `suspicion_score`, `incident_id` (for INCIDENT_LINK edges).
@@ -228,16 +228,16 @@ Researchers can audit exactly how each sentence is derived by reading [`src/prec
228
 
229
  ## Additional Files
230
 
231
- - **`signals/signals-NNNNN.parquet`** — 21 Parquet shards (~280 MB each), 2M rows per shard except final shard. Total ~5.9 GB.
232
  - **`signals/metadata.json`** — Per-shard summary, label/disposition distributions, top message types and streams.
233
- - **`graph/nodes.jsonl`** — 44,717 graph nodes (hosts + credentials derived from artifact src/dst/username).
234
- - **`graph/edges.jsonl`** — 9,494,858 graph edges, each with `labels` dict carrying MITRE, disposition, set_roles, etc.
235
  - **`graph/incidents.jsonl`** — Full incident records (12,361 lines, ~638 MB) with embedded `nodes`, `edges`, `leads`, and framework mappings.
236
  - **`graph/incidents_graphml/{0-f}/{incident_id}.graphml`** — **12,361 per-incident GraphML files**, sharded into 16 subdirectories by first hex char of the incident UUID (HuggingFace caps directories at 10,000 files). Each file is small (KB-MB) and loadable in Gephi, NetworkX, igraph, or DGL. Ideal for graph-based research where loading the entire dataset is impractical.
237
  - **`graph/attack_reports.jsonl`** — Natural-language threat-hunting reports (12,361 reports, ~21 MB). See [Attack Reports](#attack-reports).
238
  - **`reference/lead_rules_catalog.json`** — Complete catalog of 261 WitFoo lead detection rules, 158 security products, 106 classification sets, and 216 stream-to-product mappings.
239
 
240
- **Note on the artifact-level graph:** This 41.6M-row extraction yielded fewer events than the previously-published 114M versionthe difference reflects Cassandra coordinator behaviour during full-partition extraction. Methodology and content fields are otherwise identical to the 2M version.
241
 
242
  ## Sanitization
243
 
@@ -258,7 +258,7 @@ This dataset was produced in collaboration with the University of Canterbury (Ne
258
 
259
  ## Limitations
260
 
261
- - **Label imbalance**: 99.49% benign reflects production SOC reality. Sampling strategies needed for balanced training.
262
  - **Temporal scope**: July–August 2024.
263
  - **Ground truth**: All labels derive from Precinct's automated correlation. Use the `disposition` column to stratify by analyst review level.
264
  - **Shared incidents**: The same 12,361 incidents appear in both the 2M and this dataset (incidents are stored separately from signal data; only the signal sample size differs).
 
18
  - labeled-dataset
19
  - lead-rules
20
  size_categories:
21
+ - 100M<n<1B
22
  dataset_info:
23
  - config_name: signals
24
  splits:
25
  - name: train
26
+ num_examples: 114234041
27
  configs:
28
  - config_name: signals
29
  data_files:
 
47
 
48
  ## Overview
49
 
50
+ 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 **114,234,041 sanitized security events** (signal logs) across 5 organizations and **12,361 incident provenance graphs** (47,632 nodes, 32,086,552 edges).
51
 
52
  **Available in two sizes:**
53
  - [`witfoo/precinct6-cybersecurity`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity) — 2.1M signals (smaller, faster to load; same incidents and graph methodology)
54
+ - [`witfoo/precinct6-cybersecurity-100m`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity-100m) — **114M signals (this dataset)**
55
 
56
  **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)
57
 
 
67
  ```python
68
  from datasets import load_dataset
69
 
70
+ # Load flat signal logs (114M rows across 58 Parquet shards)
71
  signals = load_dataset("witfoo/precinct6-cybersecurity-100m", "signals", split="train")
72
 
73
  # Find analyst-confirmed malicious events
 
78
  # Find suspicious events that matched detection rules but are not in confirmed incidents
79
  suspicious = signals.filter(lambda x: x["label_binary"] == "suspicious")
80
 
81
+ # Load provenance graph (47.6k nodes, 32M edges)
82
  nodes = load_dataset("witfoo/precinct6-cybersecurity-100m", "graph_nodes", split="train")
83
  edges = load_dataset("witfoo/precinct6-cybersecurity-100m", "graph_edges", split="train")
84
 
 
90
 
91
  | Label | Count | Percentage |
92
  |-------|-------|------------|
93
+ | `benign` | 113,543,372 | 99.40% |
94
+ | `suspicious` | 616,605 | 0.54% |
95
+ | `malicious` | 74,064 | 0.06% |
96
 
97
  Disposition breakdown (the raw Precinct status, exposed for ground-truth stratification — see [Ground Truth](#ground-truth-and-disposition)):
98
 
99
  | Disposition | Count | Meaning |
100
  |-------------|-------|---------|
101
+ | `Unprocessed` | 114,192,640 | No analyst review (default state for benign/suspicious + un-reviewed malicious) |
102
+ | `Disrupted` | 41,391 | SOC analyst confirmed and intervened |
103
+ | `Dismissed` | 10 | SOC analyst dismissed |
104
 
105
  ## Signal Columns
106
 
 
159
 
160
  **Three-tier labels** derived from two sources:
161
 
162
+ - **`malicious`** (74,064): Events embedded as leads inside confirmed incidents. Extracted directly from incident lead objects with suspicion scores, modus operandi, and MITRE mappings.
163
+ - **`suspicious`** (616,605): Events matching WitFoo's 261 lead detection rules but not present in confirmed incidents.
164
+ - **`benign`** (113,543,372): Events not matching any detection rules and not in any incident.
165
 
166
  ### Ground Truth and Disposition
167
 
 
176
  | `False Positive` | SOC analyst confirmed false positive | Negative — analyst rejected |
177
  | `Unprocessed` | Automated detection, no human review | Lower — Precinct-confidence only |
178
 
179
+ The `disposition_category` column buckets these into four values for easier filtering: `confirmed-malicious`, `false-positive`, `dismissed`, `automated`. For experiments where ground-truth quality matters, restrict to records where `disposition` ∈ {`Disrupted`, `Resolved`} to compare against analyst-confirmed labels. **This dataset has 41,391 records with `Disrupted` status, 10 with `Dismissed`, and the remainder with `Unprocessed` (no analyst review).**
180
 
181
  For benign and suspicious records, `disposition` is `Unprocessed` (no incident association). For malicious records, `disposition` reflects the parent incident's status at extraction time.
182
 
 
212
 
213
  | Component | Count |
214
  |-----------|-------|
215
+ | Nodes (artifact-derived hosts + credentials) | 47,632 |
216
+ | Edges (artifact `EVENT`/`NETWORK_FLOW` + incident `INCIDENT_LINK`) | 32,086,552 |
217
  | Incidents | 12,361 |
218
 
219
  Per-edge labels in `edges.jsonl` include `attack_techniques`, `attack_tactics`, `set_roles`, `lifecycle_stage`, `disposition`, `mo_name`, `suspicion_score`, `incident_id` (for INCIDENT_LINK edges).
 
228
 
229
  ## Additional Files
230
 
231
+ - **`signals/signals-NNNNN.parquet`** — 58 Parquet shards (~270 MB each), 2M rows per shard except final shard. Total ~15.7 GB.
232
  - **`signals/metadata.json`** — Per-shard summary, label/disposition distributions, top message types and streams.
233
+ - **`graph/nodes.jsonl`** — 47,632 graph nodes (hosts + credentials derived from artifact src/dst/username).
234
+ - **`graph/edges.jsonl`** — 32,086,552 graph edges, each with `labels` dict carrying MITRE, disposition, set_roles, etc.
235
  - **`graph/incidents.jsonl`** — Full incident records (12,361 lines, ~638 MB) with embedded `nodes`, `edges`, `leads`, and framework mappings.
236
  - **`graph/incidents_graphml/{0-f}/{incident_id}.graphml`** — **12,361 per-incident GraphML files**, sharded into 16 subdirectories by first hex char of the incident UUID (HuggingFace caps directories at 10,000 files). Each file is small (KB-MB) and loadable in Gephi, NetworkX, igraph, or DGL. Ideal for graph-based research where loading the entire dataset is impractical.
237
  - **`graph/attack_reports.jsonl`** — Natural-language threat-hunting reports (12,361 reports, ~21 MB). See [Attack Reports](#attack-reports).
238
  - **`reference/lead_rules_catalog.json`** — Complete catalog of 261 WitFoo lead detection rules, 158 security products, 106 classification sets, and 216 stream-to-product mappings.
239
 
240
+ **Note on the artifact-level graph:** With 32M edges, the monolithic `graph.graphml` is intentionally **not** shipped at this scale per-incident GraphMLs and the streaming `edges.jsonl` are the recommended entry points. Methodology and content fields are otherwise identical to the 2M version.
241
 
242
  ## Sanitization
243
 
 
258
 
259
  ## Limitations
260
 
261
+ - **Label imbalance**: 99.40% benign reflects production SOC reality. Sampling strategies needed for balanced training.
262
  - **Temporal scope**: July–August 2024.
263
  - **Ground truth**: All labels derive from Precinct's automated correlation. Use the `disposition` column to stratify by analyst review level.
264
  - **Shared incidents**: The same 12,361 incidents appear in both the 2M and this dataset (incidents are stored separately from signal data; only the signal sample size differs).
graph/nodes.jsonl CHANGED
The diff for this file is too large to render. See raw diff
 
signals/metadata.json CHANGED
@@ -1,120 +1,120 @@
1
  {
2
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3
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4
- "total_size_bytes": 6285096828,
5
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6
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7
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8
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9
  },
10
  "disposition_distribution": {
11
- "Unprocessed": 41575946,
12
- "Disrupted": 82782,
13
- "Dismissed": 20
14
  },
15
  "top_message_types": {
16
- "security_audit_event": 9229251,
 
 
 
 
 
17
  "4662": 2812876,
 
18
  "5156": 2432714,
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  "4656": 1014502,
 
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- "ListClusters": 499015,
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66
  },
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  "top_streams": {
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- "aws_cloudtrail_events": 16087719,
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- "crond": 432131,
 
 
 
 
74
  "sshd": 248142,
75
  "filebeat_diagnostic": 240546,
76
  "network_communication": 233061,
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- "mssql$paygsql01": 193458,
 
 
79
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  "ad_audit_plus": 33017,
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- "systemd": 26286,
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- "service control manager": 23244,
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- "Integrations Status": 7192,
95
- "Crowdstrike Asset Inventory": 6994,
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- "DUO Asset Inventory": 3767,
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  "esent": 3704,
 
98
  "generic_log": 3379,
99
  "directory synchronization": 3121,
 
 
100
  "schannel": 2137,
101
- "microsoft-windows-security-spp": 2039,
102
- "Crowdstrike Detection": 1155,
 
 
 
 
103
  "mssqlserver": 529,
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- "trend micro_deep security manager": 353,
105
  "sentinelone": 337,
106
- "microsoft-windows-certificateservicesclient-autoenrollment": 293,
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118
  },
119
  "schema": [
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  "timestamp",
 
1
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+ "total_records": 114234041,
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+ "meraki": 18209,
97
+ "Crowdstrike Asset Inventory": 16605,
98
+ "Integrations Status": 12080,
99
+ "DUO Asset Inventory": 4902,
100
+ "pan_firewall": 4149,
101
  "esent": 3704,
102
+ "Crowdstrike Detection": 3530,
103
  "generic_log": 3379,
104
  "directory synchronization": 3121,
105
+ "Microsoft Graph Asset Inventory": 2200,
106
+ "microsoft-windows-security-spp": 2142,
107
  "schannel": 2137,
108
+ "barracuda_ess": 1805,
109
+ "trend micro_deep security manager": 1046,
110
+ "rsyslogd": 988,
111
+ "sqlagent$paygsql01": 878,
112
+ "apache_access_log": 821,
113
+ "symantec_dlp": 714,
114
  "mssqlserver": 529,
115
+ ".net runtime": 426,
116
  "sentinelone": 337,
117
+ "microsoft-windows-was": 308
 
 
 
 
 
 
 
 
 
 
 
118
  },
119
  "schema": [
120
  "timestamp",