README.md

metadata

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.

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:

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
• Source card title / publisher: CYB007 — Synthetic Insider Threat Dataset (Sample)
• License: cc-by-nc-4.0 (as declared by the original dataset).