Replace dataset with 41.6M-pipeline output (UC fixes) — phase 1
Browse filesPhase 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)
- README.md +23 -23
- graph/nodes.jsonl +0 -0
- signals/metadata.json +87 -87
README.md
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@@ -18,12 +18,12 @@ tags:
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- labeled-dataset
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- lead-rules
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size_categories:
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-
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dataset_info:
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- config_name: signals
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splits:
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- name: train
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num_examples:
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configs:
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- config_name: signals
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data_files:
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## Overview
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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 **
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**Available in two sizes:**
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- [`witfoo/precinct6-cybersecurity`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity) — 2.1M signals (smaller, faster to load; same incidents and graph methodology)
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- [`witfoo/precinct6-cybersecurity-100m`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity-100m) — **
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**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)
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```python
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from datasets import load_dataset
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# Load flat signal logs (
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signals = load_dataset("witfoo/precinct6-cybersecurity-100m", "signals", split="train")
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# Find analyst-confirmed malicious events
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# Find suspicious events that matched detection rules but are not in confirmed incidents
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suspicious = signals.filter(lambda x: x["label_binary"] == "suspicious")
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# Load provenance graph (
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nodes = load_dataset("witfoo/precinct6-cybersecurity-100m", "graph_nodes", split="train")
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edges = load_dataset("witfoo/precinct6-cybersecurity-100m", "graph_edges", split="train")
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| Label | Count | Percentage |
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|-------|-------|------------|
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| `benign` |
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| `
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| `
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Disposition breakdown (the raw Precinct status, exposed for ground-truth stratification — see [Ground Truth](#ground-truth-and-disposition)):
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| Disposition | Count | Meaning |
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|-------------|-------|---------|
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| `Unprocessed` |
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| `Disrupted` |
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| `Dismissed` |
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## Signal Columns
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**Three-tier labels** derived from two sources:
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- **`malicious`** (
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- **`suspicious`** (
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- **`benign`** (
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### Ground Truth and Disposition
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| `False Positive` | SOC analyst confirmed false positive | Negative — analyst rejected |
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| `Unprocessed` | Automated detection, no human review | Lower — Precinct-confidence only |
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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
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For benign and suspicious records, `disposition` is `Unprocessed` (no incident association). For malicious records, `disposition` reflects the parent incident's status at extraction time.
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| Component | Count |
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|-----------|-------|
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| Nodes (artifact-derived hosts + credentials) |
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| Edges (artifact `EVENT`/`NETWORK_FLOW` + incident `INCIDENT_LINK`) |
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| Incidents | 12,361 |
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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).
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## Additional Files
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- **`signals/signals-NNNNN.parquet`** —
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- **`signals/metadata.json`** — Per-shard summary, label/disposition distributions, top message types and streams.
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- **`graph/nodes.jsonl`** —
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- **`graph/edges.jsonl`** —
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- **`graph/incidents.jsonl`** — Full incident records (12,361 lines, ~638 MB) with embedded `nodes`, `edges`, `leads`, and framework mappings.
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- **`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.
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- **`graph/attack_reports.jsonl`** — Natural-language threat-hunting reports (12,361 reports, ~21 MB). See [Attack Reports](#attack-reports).
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- **`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.
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**Note on the artifact-level graph:**
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## Sanitization
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## Limitations
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- **Label imbalance**: 99.
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- **Temporal scope**: July–August 2024.
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- **Ground truth**: All labels derive from Precinct's automated correlation. Use the `disposition` column to stratify by analyst review level.
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- **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).
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- labeled-dataset
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- lead-rules
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size_categories:
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- 100M<n<1B
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dataset_info:
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- config_name: signals
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splits:
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- name: train
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num_examples: 114234041
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configs:
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- config_name: signals
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data_files:
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## Overview
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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).
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**Available in two sizes:**
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- [`witfoo/precinct6-cybersecurity`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity) — 2.1M signals (smaller, faster to load; same incidents and graph methodology)
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- [`witfoo/precinct6-cybersecurity-100m`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity-100m) — **114M signals (this dataset)**
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**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)
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```python
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from datasets import load_dataset
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# Load flat signal logs (114M rows across 58 Parquet shards)
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signals = load_dataset("witfoo/precinct6-cybersecurity-100m", "signals", split="train")
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# Find analyst-confirmed malicious events
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# Find suspicious events that matched detection rules but are not in confirmed incidents
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suspicious = signals.filter(lambda x: x["label_binary"] == "suspicious")
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# Load provenance graph (47.6k nodes, 32M edges)
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nodes = load_dataset("witfoo/precinct6-cybersecurity-100m", "graph_nodes", split="train")
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edges = load_dataset("witfoo/precinct6-cybersecurity-100m", "graph_edges", split="train")
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| Label | Count | Percentage |
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|-------|-------|------------|
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| `benign` | 113,543,372 | 99.40% |
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| `suspicious` | 616,605 | 0.54% |
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| `malicious` | 74,064 | 0.06% |
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Disposition breakdown (the raw Precinct status, exposed for ground-truth stratification — see [Ground Truth](#ground-truth-and-disposition)):
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| Disposition | Count | Meaning |
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|-------------|-------|---------|
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| `Unprocessed` | 114,192,640 | No analyst review (default state for benign/suspicious + un-reviewed malicious) |
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| `Disrupted` | 41,391 | SOC analyst confirmed and intervened |
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| `Dismissed` | 10 | SOC analyst dismissed |
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## Signal Columns
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**Three-tier labels** derived from two sources:
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- **`malicious`** (74,064): Events embedded as leads inside confirmed incidents. Extracted directly from incident lead objects with suspicion scores, modus operandi, and MITRE mappings.
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- **`suspicious`** (616,605): Events matching WitFoo's 261 lead detection rules but not present in confirmed incidents.
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- **`benign`** (113,543,372): Events not matching any detection rules and not in any incident.
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### Ground Truth and Disposition
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| `False Positive` | SOC analyst confirmed false positive | Negative — analyst rejected |
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| `Unprocessed` | Automated detection, no human review | Lower — Precinct-confidence only |
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+
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).**
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For benign and suspicious records, `disposition` is `Unprocessed` (no incident association). For malicious records, `disposition` reflects the parent incident's status at extraction time.
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| Component | Count |
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|-----------|-------|
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| Nodes (artifact-derived hosts + credentials) | 47,632 |
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| Edges (artifact `EVENT`/`NETWORK_FLOW` + incident `INCIDENT_LINK`) | 32,086,552 |
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| Incidents | 12,361 |
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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).
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## Additional Files
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- **`signals/signals-NNNNN.parquet`** — 58 Parquet shards (~270 MB each), 2M rows per shard except final shard. Total ~15.7 GB.
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- **`signals/metadata.json`** — Per-shard summary, label/disposition distributions, top message types and streams.
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- **`graph/nodes.jsonl`** — 47,632 graph nodes (hosts + credentials derived from artifact src/dst/username).
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- **`graph/edges.jsonl`** — 32,086,552 graph edges, each with `labels` dict carrying MITRE, disposition, set_roles, etc.
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- **`graph/incidents.jsonl`** — Full incident records (12,361 lines, ~638 MB) with embedded `nodes`, `edges`, `leads`, and framework mappings.
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- **`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.
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- **`graph/attack_reports.jsonl`** — Natural-language threat-hunting reports (12,361 reports, ~21 MB). See [Attack Reports](#attack-reports).
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- **`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.
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**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.
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## Sanitization
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## Limitations
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- **Label imbalance**: 99.40% benign reflects production SOC reality. Sampling strategies needed for balanced training.
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- **Temporal scope**: July–August 2024.
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- **Ground truth**: All labels derive from Precinct's automated correlation. Use the `disposition` column to stratify by analyst review level.
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- **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).
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graph/nodes.jsonl
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signals/metadata.json
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{
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"total_records":
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"label_distribution": {
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"benign":
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"suspicious":
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"malicious":
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"disposition_distribution": {
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"Unprocessed":
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"Disrupted":
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"Dismissed":
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},
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"top_message_types": {
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"security_audit_event":
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"4662": 2812876,
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"18453": 141318,
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"DescribeAddresses": 140900,
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"DescribeAutoScalingGroups": 139013,
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"DescribeImages": 135742,
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"GenerateDataKey": 135246,
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"DescribeInternetGateways": 131041,
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"auth_event": 126943
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},
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"top_streams": {
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"sshd": 248142,
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"filebeat_diagnostic": 240546,
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"network_communication": 233061,
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"pam": 227289,
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"systemd_logind": 126943,
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"linux_audit": 90726,
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"unknown": 55441,
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"security": 53331,
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"cisco_os": 51551,
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"ad fs auditing": 40738,
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"symantec_sep": 36946,
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"ad_audit_plus": 33017,
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"esent": 3704,
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"generic_log": 3379,
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"directory synchronization": 3121,
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"schannel": 2137,
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"mssqlserver": 529,
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"sentinelone": 337,
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"microsoft-windows-
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"meraki_l7_firewall": 292,
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"sqlagent$paygsql01": 264,
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"mssql$microsoft##wid": 251,
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"symantec_dlp": 221,
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"volsnap": 187,
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"vss": 187,
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"nutanix guest agent": 154,
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"vssaudit": 146,
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".net runtime": 135,
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"msiinstaller": 123,
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"adsync": 106
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},
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"schema": [
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"timestamp",
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{
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"total_records": 114234041,
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"n_shards": 58,
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"total_size_bytes": 16903246723,
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"label_distribution": {
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"benign": 113543372,
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"suspicious": 616605,
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"malicious": 74064
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},
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"disposition_distribution": {
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"Unprocessed": 114192640,
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"Disrupted": 41391,
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"Dismissed": 10
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},
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"top_message_types": {
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"security_audit_event": 31771712,
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"audit_log": 6347890,
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"AssumeRole": 5975795,
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"DescribeInstanceStatus": 5432873,
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| 66 |
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| 98 |
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|
| 118 |
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| 119 |
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