Datasets:

THULab
/

cyb010_sample

license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - tsfile
  - timeseries
  - cybersecurity
  - siem
  - security-logs
  - mitre-attack
  - apt
  - synthetic-data
  - alert-triage
  - soc-operations
  - threat-detection
  - splunk
  - time-series
  - format:tsfile
pretty_name: CYB010 — Synthetic Security Event Log Dataset (Sample) (TsFile)
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: alert_records
        path: alert_records.tsfile
      - split: host_inventory
        path: host_inventory.tsfile
      - split: incident_summary
        path: incident_summary.tsfile
      - split: security_events
        path: security_events.tsfile

CYB010 — Synthetic Security Event Log Dataset (Sample) (TsFile)

Apache TsFile version of xpertsystems/cyb010-sample.

Overview

CYB010 — Synthetic Security Event Log Dataset (Sample) — This is a free preview of the full CYB010 — Synthetic Security Event Log Dataset product. It contains roughly ~10% of the full dataset at identical schema, MITRE ATT&CK technique coverage, and statistical fingerprint, so you can evaluate fit before licensing the full product.

Tables: alert_records, host_inventory, incident_summary, security_events
Rows: 27,958 across 4 TsFile table(s).
- alert_records: 5,162 rows, 2024-01-02 20:47:00.595000 → 2025-03-23 01:53:19.867000 (UTC)
- host_inventory: 400 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
- incident_summary: 500 rows, 2024-01-01 00:56:18.777000 → 2024-12-30 18:22:58.977000 (UTC)
- security_events: 21,896 rows, 2024-01-01 00:56:29.283000 → 2025-04-10 03:36:47.436000 (UTC)

Schema (TsFile structure)

`alert_records.tsfile`

Time (INT64, milliseconds) — the source timestamp timestamp as INT64 milliseconds.
TAG: host_id — device dimension(s); query one device with WHERE host_id=....
FIELD (19): alert_id (string), correlated_event_ids (string), alert_rule_id (string), alert_rule_name (string), user_id (string), threat_actor_id (string), severity_level (string), alert_category (string), mitre_technique_id (string), mitre_tactic (string), label_true_positive (int64), label_false_positive (int64), analyst_triage_priority (string), suppressed_flag (int64), suppression_reason (string), time_to_detect_seconds (double), siem_platform_type (string), correlated_chain_length (int64), incident_id (string)

`host_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: host_id — device dimension(s); query one device with WHERE host_id=....
FIELD (14): hostname (string), ip_address (string), os_type (string), os_version (string), host_role (string), network_segment (string), defender_posture_tier (string), edr_agent_installed (int64), patch_compliance_level (double), criticality_rating (string), cloud_provider (string), cloud_region (string), vulnerability_count_open (int64), siem_platform (string)

`incident_summary.tsfile`

Time (INT64, milliseconds) — the source campaign_start_timestamp timestamp as INT64 milliseconds.
TAG: incident_id — device dimension(s); query one device with WHERE incident_id=....
FIELD (22): threat_actor_id (string), threat_actor_profile (string), campaign_end_timestamp (string), dwell_time_hours (double), attack_lifecycle_phase_reached (string), total_events_generated (int64), total_alerts_triggered (int64), true_positive_alerts (int64), false_positive_alerts (int64), alert_suppression_count (int64), hosts_compromised (int64), lateral_movement_hops (int64), persistence_mechanisms_installed (int64), data_volume_staged_gb (double), exfiltration_attempted_flag (int64), detection_outcome (string), time_to_detect_hours (double), mitre_tactics_observed (string), incident_success_flag (int64), defender_posture_tier (string), false_positive_rate_campaign (double), noise_signal_ratio (double)

`security_events.tsfile`

Time (INT64, milliseconds) — the source timestamp timestamp as INT64 milliseconds.
TAG: host_id — device dimension(s); query one device with WHERE host_id=....
FIELD (21): event_id (string), user_id (string), source_ip (string), dest_ip (string), source_port (int64), dest_port (int64), event_class (string), event_type (string), log_source_type (string), severity_level (string), attack_lifecycle_phase (string), mitre_technique_id (string), mitre_tactic (string), label_malicious (int64), label_false_positive (int64), label_log_tampered (int64), threat_actor_id (string), threat_actor_profile (string), cvss_score_analogue (double), raw_log_payload (string), incident_id (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("alert_records.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/cyb010-sample
Source card title / publisher: CYB010 — Synthetic Security Event Log Dataset (Sample)
License: cc-by-nc-4.0 (as declared by the original dataset).