README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- time-series-forecasting
tags:
- tsfile
- timeseries
- cybersecurity
- vulnerability-management
- cve
- cvss
- epss
- cisa-kev
- synthetic-data
- patch-management
- supply-chain-security
- zero-day
- time-series
- format:tsfile
pretty_name: CYB009 — Synthetic Vulnerability Intelligence (Sample) (TsFile)
size_categories:
- 100K<n<1M
configs:
- config_name: default
  data_files:
  - split: asset_inventory
    path: asset_inventory.tsfile
  - split: vuln_lifecycle_events
    path: vuln_lifecycle_events.tsfile
  - split: vuln_summary
    path: vuln_summary.tsfile
  - split: vulnerability_records
    path: vulnerability_records.tsfile
---

# CYB009 — Synthetic Vulnerability Intelligence (Sample) (TsFile)

Apache TsFile version of [`xpertsystems/cyb009-sample`](https://huggingface.co/datasets/xpertsystems/cyb009-sample).

## Overview

**CYB009 — Synthetic Vulnerability Intelligence Dataset (Sample)** — This is a **free preview** of the full **CYB009 — Synthetic Vulnerability Intelligence Dataset** product. It contains roughly **~65% of the full dataset rows** (but generated from ~40% the org/asset count) at identical schema, CVSS distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.

- **Tables:** `asset_inventory`, `vuln_lifecycle_events`, `vuln_summary`, `vulnerability_records`
- **Rows:** 349,257 across 4 TsFile table(s).
  - `asset_inventory`: 1,280 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
  - `vuln_lifecycle_events`: 28,779 rows — Time is the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
  - `vuln_summary`: 2,638 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
  - `vulnerability_records`: 316,560 rows — Time is the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).

## Schema (TsFile structure)

### `asset_inventory.tsfile`

- **Time** (INT64, milliseconds) — synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
- **TAG**: `asset_id` — device dimension(s); query one device with `WHERE asset_id=...`.
- **FIELD** (13): `org_id` (string), `asset_type` (string), `criticality_tier` (string), `environment_type` (string), `os_family` (string), `scanner_coverage` (double), `patch_mgmt_maturity` (double), `mean_time_to_remediate_days` (double), `sla_critical_days` (int64), `sla_high_days` (int64), `sla_medium_days` (int64), `internet_exposed_flag` (int64), `sbom_depth_score` (double)

### `vuln_lifecycle_events.tsfile`

- **Time** (INT64, milliseconds) — the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
- **TAG**: `asset_id` — device dimension(s); query one device with `WHERE asset_id=...`.
- **FIELD** (6): `vuln_id` (string), `org_id` (string), `event_type` (string), `severity_class` (string), `scanner_id` (string), `patch_vendor_id` (string)

### `vuln_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**: `asset_id` — device dimension(s); query one device with `WHERE asset_id=...`.
- **FIELD** (20): `vuln_id` (string), `org_id` (string), `vulnerability_class` (string), `severity_class` (string), `cvss_base_score` (double), `cvss_temporal_score_final` (double), `epss_score_final` (double), `exploit_maturity_final` (string), `patch_lag_days` (int64), `time_to_remediate_days` (int64), `time_to_exploit_days` (int64), `sla_compliance_flag` (int64), `exploitation_occurred_flag` (int64), `zero_day_flag` (int64), `remediation_success_flag` (int64), `risk_score_composite` (double), `compensating_control_flag` (int64), `supply_chain_propagation_flag` (int64), `cisa_kev_flag` (int64), `false_positive_flag` (int64)

### `vulnerability_records.tsfile`

- **Time** (INT64, milliseconds) — the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
- **TAG**: `asset_id` — device dimension(s); query one device with `WHERE asset_id=...`.
- **FIELD** (14): `vuln_id` (string), `org_id` (string), `lifecycle_phase` (string), `cvss_base_score` (double), `cvss_temporal_score` (double), `cvss_environmental_score` (double), `epss_score` (double), `exploit_maturity` (string), `patch_status` (string), `remediation_status` (string), `days_since_discovery` (int64), `days_since_patch_release` (int64), `asset_criticality_tier` (string), `vulnerability_class` (string)

## 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("asset_inventory.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/cyb009-sample`](https://huggingface.co/datasets/xpertsystems/cyb009-sample)
- Source card title / publisher: CYB009 — Synthetic Vulnerability Intelligence (Sample)
- License: cc-by-nc-4.0 (as declared by the original dataset).