---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- time-series-forecasting
tags:
- tsfile
- timeseries
- cybersecurity
- network-traffic
- intrusion-detection
- synthetic-data
- anomaly-detection
- apt
- c2-beacon
- time-series
- format:tsfile
pretty_name: CYB001 — Synthetic Network Traffic (Sample) (TsFile)
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: flow_events
    path: flow_events.tsfile
  - split: network_flows
    path: network_flows.tsfile
  - split: network_topology
    path: network_topology.tsfile
  - split: session_summary
    path: session_summary.tsfile
---

# CYB001 — Synthetic Network Traffic (Sample) (TsFile)

Apache TsFile version of [`xpertsystems/cyb001-sample`](https://huggingface.co/datasets/xpertsystems/cyb001-sample).

## Overview

**CYB001 — Synthetic Network Traffic Dataset (Sample)** — This is a **free preview** of the full **CYB001 — Synthetic Network Traffic Dataset** product. It contains roughly **1 / 60th of the full dataset** at identical schema, label distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.

- **Tables:** `flow_events`, `network_flows`, `network_topology`, `session_summary`
- **Rows:** 16,401 across 4 TsFile table(s).
  - `flow_events`: 5,431 rows — Time is the source `timestamp_offset_ms` offset in milliseconds.
  - `network_flows`: 9,770 rows, 2024-01-01 01:12:21.832000 → 2024-01-30 23:52:14.441000 (UTC)
  - `network_topology`: 200 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
  - `session_summary`: 1,000 rows, 2024-01-01 01:10:27 → 2024-01-30 23:47:40 (UTC)

## Schema (TsFile structure)

### `flow_events.tsfile`

- **Time** (INT64, milliseconds) — the source `timestamp_offset_ms` offset in milliseconds.
- **TAG**: `segment_id` — device dimension(s); query one device with `WHERE segment_id=...`.
- **FIELD** (9): `flow_id` (string), `session_id` (string), `event_type` (string), `source_port` (int64), `dest_port` (int64), `protocol` (string), `packet_size_bytes` (int64), `payload_entropy` (double), `event_severity` (string)

### `network_flows.tsfile`

- **Time** (INT64, milliseconds) — the source `flow_start_timestamp` timestamp as INT64 milliseconds.
- **TAG**: `segment_id` — device dimension(s); query one device with `WHERE segment_id=...`.
- **FIELD** (36): `flow_id` (string), `session_id` (string), `source_ip_hash` (string), `destination_ip_hash` (string), `source_port` (int64), `dest_port` (int64), `protocol` (string), `flow_duration_ms` (int64), `total_fwd_packets` (int64), `total_bwd_packets` (int64), `total_bytes_fwd` (int64), `total_bytes_bwd` (int64), `fwd_packet_len_mean` (int64), `fwd_packet_len_std` (int64), `bwd_packet_len_mean` (int64), `bwd_packet_len_std` (int64), `flow_bytes_per_sec` (double), `flow_packets_per_sec` (double), `inter_arrival_time_mean` (double), `inter_arrival_time_std` (double), `tcp_flag_syn_count` (int64), `tcp_flag_ack_count` (int64), `tcp_flag_fin_count` (int64), `tcp_flag_rst_count` (int64), `tcp_flag_psh_count` (int64), `tcp_flag_urg_count` (int64), `flow_lifecycle_phase` (string), `traffic_category` (string), `attack_subcategory` (string), `label` (string), `source_device_type` (string), `dest_device_type` (string), `attacker_capability_tier` (string), `retransmission_flag` (int64), `fragmentation_flag` (int64), `protocol_violation_flag` (int64)

### `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): `detector_id` (string), `segment_type` (string), `trust_level` (double), `firewall_policy` (string), `allowed_protocols` (string), `avg_concurrent_flows` (int64), `bandwidth_mbps` (double), `nat_enabled` (int64), `ids_coverage` (double), `qos_policy` (string), `diurnal_peak_factor` (double), `feature_space_dim` (int64), `alert_threshold` (double), `retraining_cadence_days` (int64), `ensemble_size` (int64), `defender_architecture` (string), `device_count` (int64)

### `session_summary.tsfile`

- **Time** (INT64, milliseconds) — the source `session_start_timestamp` timestamp as INT64 milliseconds.
- **TAG**: `segment_id` — device dimension(s); query one device with `WHERE segment_id=...`.
- **FIELD** (22): `session_id` (string), `source_ip_hash` (string), `destination_ip_hash` (string), `session_duration_sec` (double), `total_flows` (int64), `total_bytes_transferred` (int64), `total_packets` (int64), `dominant_protocol` (string), `primary_traffic_category` (string), `label` (string), `attack_subcategory` (string), `attacker_capability_tier` (string), `source_device_type` (string), `dest_device_type` (string), `payload_entropy_mean` (double), `retransmission_rate` (double), `protocol_violation_count` (int64), `c2_beacon_flag` (int64), `exfil_volume_bytes` (double), `scan_probe_count` (int64), `lateral_move_flag` (int64), `session_risk_score` (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("flow_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/cyb001-sample`](https://huggingface.co/datasets/xpertsystems/cyb001-sample)
- Source card title / publisher: CYB001 — Synthetic Network Traffic (Sample)
- License: cc-by-nc-4.0 (as declared by the original dataset).