README.md

metadata

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.

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:

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
• Source card title / publisher: CYB009 — Synthetic Vulnerability Intelligence (Sample)
• License: cc-by-nc-4.0 (as declared by the original dataset).