Datasets:

THULab
/

cyb002_sample

license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - tsfile
  - timeseries
  - cybersecurity
  - mitre-attack
  - kill-chain
  - apt
  - ransomware
  - synthetic-data
  - threat-modeling
  - red-team
  - blue-team
  - time-series
  - format:tsfile
pretty_name: CYB002 — Synthetic Cyber Attack Dataset (Sample) (TsFile)
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: attack_events
        path: attack_events.tsfile
      - split: campaign_events
        path: campaign_events.tsfile
      - split: campaign_summary
        path: campaign_summary.tsfile
      - split: network_topology
        path: network_topology.tsfile

CYB002 — Synthetic Cyber Attack Dataset (Sample) (TsFile)

Apache TsFile version of xpertsystems/cyb002-sample.

Overview

CYB002 — Synthetic Cyber Attack Dataset (Sample) — This is a free preview of the full CYB002 — Synthetic Cyber Attack Dataset product. It contains roughly 1 / 60th of the full dataset at identical schema, attacker-tier distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.

Tables: attack_events, campaign_events, campaign_summary, network_topology
Rows: 5,843 across 4 TsFile table(s).
- attack_events: 4,353 rows — Time is the source timestep simulation step counter, stored as milliseconds (1 step = 1 ms).
- campaign_events: 739 rows — Time is the source timestep simulation step counter, stored as milliseconds (1 step = 1 ms).
- campaign_summary: 100 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
- network_topology: 651 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)

`attack_events.tsfile`

Time (INT64, milliseconds) — the source timestep simulation step counter, stored as milliseconds (1 step = 1 ms).
TAG: campaign_id — device dimension(s); query one device with WHERE campaign_id=....
FIELD (22): attacker_id (string), kill_chain_phase (string), technique_id (string), technique_name (string), tactic_category (string), target_segment_id (string), target_asset_type (string), source_ip_class (string), dest_port (int64), protocol (string), bytes_transferred (double), connection_duration_s (double), auth_failure_count (int64), process_injection_flag (int64), lateral_hop_count (int64), c2_beacon_interval_s (double), detection_outcome (string), alert_severity (string), siem_rule_triggered (int64), edr_blocked_flag (int64), attacker_capability_tier (string), defender_maturity_level (string)

`campaign_events.tsfile`

Time (INT64, milliseconds) — the source timestep simulation step counter, stored as milliseconds (1 step = 1 ms).
TAG: campaign_id — device dimension(s); query one device with WHERE campaign_id=....
FIELD (6): attacker_id (string), event_type (string), target_segment_id (string), target_asset_id (string), responder_id (string), related_campaign_id (string)

`campaign_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: campaign_id — device dimension(s); query one device with WHERE campaign_id=....
FIELD (30): attacker_id (string), attacker_capability_tier (string), campaign_type (string), target_environment_type (string), kill_chain_phases_reached (string), highest_phase_achieved (string), lateral_hops_total (int64), assets_compromised_count (int64), data_exfil_gb (double), dwell_time_hours (double), detection_rate (double), alert_volume_total (int64), suppressed_alert_count (int64), edr_block_count (int64), ir_escalation_count (int64), campaign_duration_steps (int64), campaign_success_flag (int64), ransomware_deployed_flag (int64), c2_sessions_established (int64), technique_count_unique (int64), tactic_breadth_score (double), attacker_stealth_score (double), defender_response_score (double), mttd_hours (double), mttr_hours (double), coordinated_attack_flag (int64), exfiltration_success_flag (int64), privesc_success_flag (int64), phase_detection_coverage (double), attacker_roi_score (double)

`network_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: segment_id — device dimension(s); query one device with WHERE segment_id=....
FIELD (17): asset_id (string), segment_type (string), asset_type (string), os_family (string), patch_lag_days (int64), exposure_score (double), defender_maturity_level (string), siem_coverage_flag (int64), edr_deployed_flag (int64), ndr_coverage_flag (int64), mfa_enforced_flag (int64), vulnerability_count (int64), trust_zone (string), inter_segment_trust_level (double), alert_threshold_sensitivity (double), mttd_baseline_hours (double), mttr_baseline_hours (double)

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("attack_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/cyb002-sample
Source card title / publisher: CYB002 — Synthetic Cyber Attack Dataset (Sample)
License: cc-by-nc-4.0 (as declared by the original dataset).