README.md

---
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`](https://huggingface.co/datasets/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:

```python
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`](https://huggingface.co/datasets/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).