Datasets:
File size: 6,705 Bytes
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license: cc-by-nc-4.0
task_categories:
- tabular-classification
- time-series-forecasting
tags:
- tsfile
- timeseries
- cybersecurity
- insider-threat
- ueba
- data-exfiltration
- synthetic-data
- privileged-access
- hr-analytics
- dlp
- zero-trust
- behavioral-analytics
- time-series
- format:tsfile
pretty_name: CYB007 — Synthetic Insider Threat Dataset (Sample) (TsFile)
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: incident_events
path: incident_events.tsfile
- split: incident_summary
path: incident_summary.tsfile
- split: insider_trajectories
path: insider_trajectories.tsfile
- split: org_topology
path: org_topology.tsfile
---
# CYB007 — Synthetic Insider Threat Dataset (Sample) (TsFile)
Apache TsFile version of [`xpertsystems/cyb007-sample`](https://huggingface.co/datasets/xpertsystems/cyb007-sample).
## Overview
**CYB007 — Synthetic Insider Threat Dataset (Sample)** — This is a **free preview** of the full **CYB007 — Synthetic Insider Threat Dataset** product. It contains roughly **~10% of the full dataset** at identical schema, actor-tier distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.
- **Tables:** `incident_events`, `incident_summary`, `insider_trajectories`, `org_topology`
- **Rows:** 71,927 across 4 TsFile table(s).
- `incident_events`: 38,687 rows — Time is the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
- `incident_summary`: 500 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
- `insider_trajectories`: 32,500 rows — Time is the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
- `org_topology`: 240 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
## Schema (TsFile structure)
### `incident_events.tsfile`
- **Time** (INT64, milliseconds) — the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
- **TAG**: `incident_id` — device dimension(s); query one device with `WHERE incident_id=...`.
- **FIELD** (5): `actor_id` (string), `event_type` (string), `target_department_id` (string), `dlp_system_id` (string), `colluding_actor_id` (string)
### `incident_summary.tsfile`
- **Time** (INT64, milliseconds) — synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
- **TAG**: `incident_id` — device dimension(s); query one device with `WHERE incident_id=...`.
- **FIELD** (27): `actor_id` (string), `actor_threat_type` (string), `target_environment_type` (string), `incident_type` (string), `total_data_accessed_mb` (double), `exfiltration_attempts_total` (int64), `exfiltration_successes` (int64), `exfiltration_success_rate` (double), `detection_rate` (double), `exfiltration_volume_total_mb` (double), `avg_data_access_velocity` (double), `max_privilege_escalation_depth` (int64), `incident_duration_steps` (int64), `lateral_access_count` (int64), `dlp_blind_spots_identified` (int64), `cover_tracks_events` (int64), `hr_case_triggers_caused` (int64), `incident_success_flag` (int64), `attribution_risk_score` (double), `stealth_score` (double), `actor_efficiency_score` (double), `sabotage_events_executed` (int64), `colluding_actor_count` (int64), `coordinated_incident_flag` (int64), `cover_tracks_effectiveness` (double), `organisational_impact_score` (double), `exfiltration_roi_score` (double)
### `insider_trajectories.tsfile`
- **Time** (INT64, milliseconds) — the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
- **TAG**: `incident_id` — device dimension(s); query one device with `WHERE incident_id=...`.
- **FIELD** (11): `actor_id` (string), `incident_phase` (string), `data_access_volume_mb` (double), `privilege_event_count` (int64), `communication_anomaly_score` (double), `dlp_confidence_score` (double), `detection_outcome` (string), `exfiltration_volume_mb_cumulative` (double), `behavioural_risk_score` (double), `target_data_sensitivity_tier` (string), `actor_threat_type` (string)
### `org_topology.tsfile`
- **Time** (INT64, milliseconds) — synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
- **TAG**: `department_id` — device dimension(s); query one device with `WHERE department_id=...`.
- **FIELD** (9): `dlp_system_id` (string), `department_type` (string), `data_sensitivity_tier` (string), `dlp_coverage` (double), `ueba_deployment_status` (string), `privileged_role_density` (double), `alert_threshold` (double), `hr_integration_status` (int64), `pam_coverage` (int64)
## Conversion notes
- Each CSV of the sample is converted into its own TsFile table (4 tables), so no table is merged with another and no column is dropped.
- Event/telemetry tables use the source simulation clock as Time (INT64 milliseconds): `timestep` step counters and millisecond offsets are stored as-is, minute-based columns are multiplied by 60,000, and ISO/UTC strings are parsed to epoch milliseconds.
- Static inventory/topology/summary tables have no per-row clock; their Time is a synthetic row index (0..N-1) over the stable source order.
- Low-cardinality dimensions of each table (campaign/incident/asset/session ids, phases, labels) are declared as TAG columns; query one simulation with e.g. `WHERE campaign_id='...'`.
## Usage
Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
```python
from pathlib import Path
from tsfile import TsFileReader
path = Path("incident_events.tsfile")
with TsFileReader(str(path)) as reader:
schemas = reader.get_all_table_schemas()
print("tables:", list(schemas))
table_name = next(iter(schemas))
table = schemas[table_name]
columns = [column.get_column_name() for column in table.get_columns()]
print("columns:", columns)
field_names = [
column.get_column_name()
for column in table.get_columns()
if column.get_column_name() not in {"Time", "time"}
]
if field_names:
with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
batch = result.read_arrow_batch()
if batch is not None:
print(batch.to_pandas().head())
```
## Source & license
- Original dataset: [`xpertsystems/cyb007-sample`](https://huggingface.co/datasets/xpertsystems/cyb007-sample)
- Source card title / publisher: CYB007 — Synthetic Insider Threat Dataset (Sample)
- License: cc-by-nc-4.0 (as declared by the original dataset).
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