---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- time-series-forecasting
tags:
- tsfile
- timeseries
- cybersecurity
- adversarial-machine-learning
- ai-security
- adversarial-attacks
- evasion-attacks
- apt
- synthetic-data
- ml-security
- model-robustness
- mlops-security
- time-series
- format:tsfile
pretty_name: CYB011 — Synthetic AI Evasion Attack Trajectories (Sample) (TsFile)
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: attack_trajectories
    path: attack_trajectories.tsfile
  - split: campaign_events
    path: campaign_events.tsfile
  - split: campaign_summary
    path: campaign_summary.tsfile
  - split: network_topology
    path: network_topology.tsfile
---

# CYB011 — Synthetic AI Evasion Attack Trajectories (Sample) (TsFile)

Apache TsFile version of [`xpertsystems/cyb011-sample`](https://huggingface.co/datasets/xpertsystems/cyb011-sample).

## Overview

**CYB011 — Synthetic AI Evasion Attack Trajectory Dataset (Sample)** — This is a **free preview** of the full **CYB011 — Synthetic AI Evasion Attack Trajectory Dataset** product. It contains roughly **~4% 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_trajectories`, `campaign_events`, `campaign_summary`, `network_topology`
- **Rows:** 27,710 across 4 TsFile table(s).
  - `attack_trajectories`: 14,000 rows — Time is the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
  - `campaign_events`: 13,310 rows — Time is the source `timestep` simulation step counter, stored as milliseconds (1 step = 1 ms).
  - `campaign_summary`: 200 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
  - `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.

## Schema (TsFile structure)

### `attack_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): `attacker_id` (string), `attack_phase` (string), `perturbation_magnitude` (double), `feature_delta_l2_norm` (double), `feature_delta_linf_norm` (double), `detector_confidence_score` (double), `detection_outcome` (string), `query_count_cumulative` (int64), `evasion_budget_consumed` (double), `target_segment_id` (string), `attacker_capability_tier` (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** (5): `attacker_id` (string), `event_type` (string), `target_segment_id` (string), `detector_id` (string), `partner_attacker_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** (24): `attacker_id` (string), `attacker_capability_tier` (string), `target_environment_type` (string), `campaign_type` (string), `total_queries_issued` (int64), `evasion_attempts_total` (int64), `evasion_successes` (int64), `evasion_success_rate` (double), `detection_rate` (double), `perturbation_budget_consumed` (double), `avg_perturbation_magnitude` (double), `max_perturbation_linf` (double), `campaign_duration_steps` (int64), `lateral_pivot_count` (int64), `detector_blind_spots_mapped` (int64), `concept_drift_events` (int64), `retrain_triggers_caused` (int64), `campaign_success_flag` (int64), `attribution_risk_score` (double), `stealth_score` (double), `attack_efficiency_score` (double), `model_extraction_queries` (int64), `transfer_attack_count` (int64), `coordinated_attack_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** (11): `detector_id` (string), `segment_type` (string), `trust_level` (double), `detection_coverage` (double), `defender_architecture` (string), `feature_space_dim` (int64), `alert_threshold` (double), `retraining_cadence_days` (int64), `ensemble_size` (int64), `detection_strength` (double), `adversarial_robustness` (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_trajectories.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/cyb011-sample`](https://huggingface.co/datasets/xpertsystems/cyb011-sample)
- Source card title / publisher: CYB011 — Synthetic AI Evasion Attack Trajectories (Sample)
- License: cc-by-nc-4.0 (as declared by the original dataset).