README.md

---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- time-series-forecasting
tags:
- tsfile
- timeseries
- cybersecurity
- phishing
- bec
- email-security
- social-engineering
- threat-intelligence
- apt
- synthetic-data
- awareness-training
- mfa-bypass
- time-series
- format:tsfile
pretty_name: CYB004 — Synthetic Phishing Campaign Dataset (Sample) (TsFile)
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: campaign_events
    path: campaign_events.tsfile
  - split: campaign_summary
    path: campaign_summary.tsfile
  - split: campaign_trajectories
    path: campaign_trajectories.tsfile
  - split: victim_topology
    path: victim_topology.tsfile
---

# CYB004 — Synthetic Phishing Campaign Dataset (Sample) (TsFile)

Apache TsFile version of [`xpertsystems/cyb004-sample`](https://huggingface.co/datasets/xpertsystems/cyb004-sample).

## Overview

**CYB004 — Synthetic Phishing Campaign Dataset (Sample)** — This is a **free preview** of the full **CYB004 — Synthetic Phishing Campaign Dataset** product. It contains roughly **1 / 48th of the full dataset** at identical schema, actor-tier distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.

- **Tables:** `campaign_events`, `campaign_summary`, `campaign_trajectories`, `victim_topology`
- **Rows:** 5,338 across 4 TsFile table(s).
  - `campaign_events`: 1,254 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.
  - `campaign_trajectories`: 3,952 rows — Time is the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
  - `victim_topology`: 32 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)

### `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** (5): `actor_id` (int64), `event_type` (string), `target_department_id` (int64), `gateway_id` (int64), `partner_actor_id` (int64)

### `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** (23): `actor_id` (int64), `actor_capability_tier` (string), `target_industry_sector` (string), `campaign_type` (string), `total_emails_sent` (int64), `click_through_events` (int64), `credential_submissions` (int64), `account_takeovers` (int64), `click_through_rate` (double), `credential_submission_rate` (double), `gateway_block_rate` (double), `campaign_duration_steps` (int64), `lateral_phishing_count` (int64), `bec_attempt_count` (int64), `mfa_bypass_attempts` (int64), `user_report_events` (int64), `infrastructure_burn_events` (int64), `campaign_success_flag` (int64), `actor_attribution_risk_score` (double), `lure_effectiveness_score` (double), `operational_stealth_score` (double), `coordinated_campaign_flag` (int64), `phishing_roi_score` (double)

### `campaign_trajectories.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** (11): `actor_id` (int64), `campaign_phase` (string), `emails_sent_cumulative` (int64), `click_through_rate` (double), `credential_submission_rate` (double), `gateway_detection_score` (double), `delivery_outcome` (string), `lure_personalisation_score` (double), `evasion_technique_active` (string), `target_department_id` (int64), `actor_capability_tier` (string)

### `victim_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** (11): `gateway_id` (int64), `department_type` (string), `industry_sector` (string), `employee_count` (int64), `privileged_account_density` (double), `awareness_training_level` (string), `mfa_enrollment_rate` (double), `gateway_architecture` (string), `dmarc_enforcement_level` (string), `click_susceptibility_base` (double), `email_volume_daily` (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:

```python
from pathlib import Path
from tsfile import TsFileReader

path = Path("campaign_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/cyb004-sample`](https://huggingface.co/datasets/xpertsystems/cyb004-sample)
- Source card title / publisher: CYB004 — Synthetic Phishing Campaign Dataset (Sample)
- License: cc-by-nc-4.0 (as declared by the original dataset).