Datasets:

THULab
/

cyb008_sample

license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - tsfile
  - timeseries
  - cybersecurity
  - soc-operations
  - alert-triage
  - mitre-attack
  - soar
  - siem
  - synthetic-data
  - incident-response
  - analyst-fatigue
  - false-positive-reduction
  - time-series
  - format:tsfile
pretty_name: CYB008 — Synthetic SOC Alert Dataset (Sample) (TsFile)
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: alert_events
        path: alert_events.tsfile
      - split: incident_summary
        path: incident_summary.tsfile
      - split: soc_alerts
        path: soc_alerts.tsfile
      - split: soc_topology
        path: soc_topology.tsfile

CYB008 — Synthetic SOC Alert Dataset (Sample) (TsFile)

Apache TsFile version of xpertsystems/cyb008-sample.

Overview

CYB008 — Synthetic SOC Alert Dataset (Sample) — This is a free preview of the full CYB008 — Synthetic SOC Alert Dataset product. It contains roughly ~10% of the full dataset at identical schema, MITRE ATT&CK tactic coverage, and statistical fingerprint, so you can evaluate fit before licensing the full product.

Tables: alert_events, incident_summary, soc_alerts, soc_topology
Rows: 65,112 across 4 TsFile table(s).
- alert_events: 55,298 rows — Time is the source event_timestamp_min simulation clock in minutes, converted to milliseconds (×60,000).
- incident_summary: 589 rows — Time is the source first_alert_timestamp_min simulation clock in minutes, converted to milliseconds (×60,000).
- soc_alerts: 9,200 rows — Time is the source alert_timestamp_min simulation clock in minutes, converted to milliseconds (×60,000).
- soc_topology: 25 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)

`alert_events.tsfile`

Time (INT64, milliseconds) — the source event_timestamp_min simulation clock in minutes, converted to milliseconds (×60,000).
TAG: analyst_id — device dimension(s); query one device with WHERE analyst_id=....
FIELD (9): event_id (string), alert_id (string), event_type (string), alert_severity (string), mitre_tactic (string), detection_source_id (string), escalation_target_tier (string), soar_playbook_id (string), partner_alert_id (string)

`incident_summary.tsfile`

Time (INT64, milliseconds) — the source first_alert_timestamp_min simulation clock in minutes, converted to milliseconds (×60,000).
TAG: incident_id — device dimension(s); query one device with WHERE incident_id=....
FIELD (21): soc_id (int64), analyst_id_lead (int64), incident_severity (string), incident_type (string), alert_count_linked (int64), incident_declared_timestamp_min (double), incident_closed_timestamp_min (double), mttd_minutes (double), mttr_minutes (double), false_positive_rate (double), escalation_count (int64), soar_actions_taken (int64), analyst_tier_peak (string), kill_chain_stages_observed (int64), mitre_tactics_covered (string), containment_action_count (int64), threat_hunt_triggered (int64), incident_success_flag (int64), analyst_fatigue_score (double), sla_breached_flag (int64), auto_resolution_fraction (double)

`soc_alerts.tsfile`

Time (INT64, milliseconds) — the source alert_timestamp_min simulation clock in minutes, converted to milliseconds (×60,000).
TAG: soc_id — device dimension(s); query one device with WHERE soc_id=....
FIELD (25): alert_id (string), incident_id (string), analyst_id (int64), shift_id (string), alert_lifecycle_phase (string), alert_severity (string), alert_source (string), mitre_tactic (string), mitre_technique_id (string), detection_rule_id (string), raw_score (double), enriched_score (double), analyst_tier (string), time_in_phase_minutes (double), queue_depth_at_ingestion (int64), soar_playbook_triggered (int64), automation_resolved (int64), escalation_flag (int64), sla_breached_flag (int64), resolution_outcome (string), mttd_minutes (double), mttr_minutes (double), fatigue_score_at_alert (double), siem_platform (string), soar_playbook_id (string)

`soc_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: soc_id — device dimension(s); query one device with WHERE soc_id=....
FIELD (12): analyst_id (int64), analyst_tier (string), shift_pattern (string), siem_platform (string), detection_source_type (string), automation_coverage_fraction (double), soar_playbook_count (int64), detection_rule_count (int64), alert_volume_daily_mean (int64), false_positive_rate_baseline (double), escalation_policy (string), max_concurrent_alerts (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:

from pathlib import Path
from tsfile import TsFileReader

path = Path("alert_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/cyb008-sample
Source card title / publisher: CYB008 — Synthetic SOC Alert Dataset (Sample)
License: cc-by-nc-4.0 (as declared by the original dataset).