README.md

---
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - cybersecurity
  - adversarial-machine-learning
  - ai-security
  - adversarial-attacks
  - evasion-attacks
  - apt
  - synthetic-data
  - ml-security
  - model-robustness
  - mlops-security
pretty_name: CYB011 — Synthetic AI Evasion Attack Trajectories (Sample)
size_categories:
  - 10K<n<100K
---

# CYB011 — Synthetic AI Evasion Attack Trajectory Dataset (Sample)

**XpertSystems.ai Synthetic Data Platform · SKU: CYB011-SAMPLE · Version 1.0.0**

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.

> 🤖 **Trained baseline + leakage diagnostic available:**
> [**xpertsystems/cyb011-baseline-classifier**](https://huggingface.co/xpertsystems/cyb011-baseline-classifier)
> — XGBoost + PyTorch MLP for **7-class adversarial attack phase
> classification** (the dataset's headline target), group-aware split
> by `campaign_id`, multi-seed evaluation (acc 0.867 ± 0.010, ROC-AUC
> 0.977 ± 0.002). **Includes a comprehensive `leakage_diagnostic.json`**
> documenting 6 oracle paths discovered across the dataset's targets,
> 4 README-suggested headline targets that are unlearnable on the
> sample after honest leak removal, and the missing `nation_state`
> attacker tier. Buyers planning adversarial ML research should read
> the diagnostic first.

| File                          | Rows (sample) | Rows (full)   | Description                                  |
|-------------------------------|---------------|---------------|----------------------------------------------|
| `network_topology.csv`        | ~200           | ~2,800        | Network segment / defender registry          |
| `campaign_summary.csv`        | ~200           | ~5,500        | Per-campaign aggregate outcomes              |
| `campaign_events.csv`         | ~13,310        | ~55,000       | Discrete campaign event log                  |
| `attack_trajectories.csv`     | ~14,000        | ~320,000      | Per-timestep adversarial trajectories        |

## Dataset Summary

CYB011 simulates end-to-end **adversarial AI evasion attack campaigns**
against ML-based security detection systems, modeled as a **6-phase
adversarial state machine** (data has 7 phases — adds `idle_dwell`):

- **6 adversarial phases**: reconnaissance → feature_space_probe →
  perturbation_craft → evasion_attempt → feedback_adaptation →
  campaign_consolidation
- **4 attacker capability tiers**: script_kiddie, opportunistic,
  advanced_persistent_threat (APT), nation_state — with per-tier
  ε-budgets (L∞ perturbation), query budgets (50 → 5,000), base evasion
  rates, and stealth weights
- **8 defender detection architectures** with per-architecture
  detection_strength (e.g. ensemble_layered 0.91, gradient_boosted 0.78,
  neural_network 0.74, isolation_forest 0.62)
- **L∞ perturbation budget modeling** — calibrated mean ε ≈ 0.185
  representing realistic imperceptibility constraints
- **Query budget tracking** — black-box vs white-box attack distinction
- **Concept drift injection** — adversarial data poisoning of training
  distributions, ~8% injection rate
- **Retraining trigger modeling** — defender model refresh after drift
  detection (~14% trigger rate)
- **Transfer attack modeling** — perturbations crafted on surrogate
  models, 31% transfer success rate
- **Honeypot density** — deception model coverage (5% baseline)
- **Coordinated multi-attacker campaigns** with 12% coordination rate
- **MLOps security signals** — gradient access patterns, feature-space
  probing, lateral pivoting between models

## Trained Baseline + Leakage Audit Available

A working baseline classifier + comprehensive leakage diagnostic is
published at
**[xpertsystems/cyb011-baseline-classifier](https://huggingface.co/xpertsystems/cyb011-baseline-classifier)**.

| Component | Detail |
|---|---|
| Primary task | **7-class `attack_phase` classification** (the dataset's headline target) |
| Secondary artifact | **`leakage_diagnostic.json`** — 6 oracle paths + 4 unlearnable targets + missing tier note |
| Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
| Features | 37 (after one-hot encoding); pipeline included as `feature_engineering.py` |
| Split | **Group-aware** (GroupShuffleSplit on `campaign_id`) — 200 campaigns, ~30 in test fold |
| Validation | Single seed + multi-seed aggregate across 10 seeds |
| Demo | `inference_example.ipynb` — end-to-end copy-paste |
| Headline metrics | XGBoost: accuracy 0.867 ± 0.010, macro ROC-AUC 0.977 ± 0.002 (multi-seed) |

**Important findings for buyers planning CYB011 ML work** (full detail
in
[`leakage_diagnostic.json`](https://huggingface.co/xpertsystems/cyb011-baseline-classifier/blob/main/leakage_diagnostic.json)):

**Missing `nation_state` attacker tier:** README lists 4 tiers; sample
contains only 3 (script_kiddie 50%, opportunistic 40%, APT 10%).
Nation_state events are entirely absent. Models trained on this
sample cannot generalize to nation_state actors.

**6 oracle paths documented** across the dataset's targets:

**Phase target oracles (3 paths — must be dropped):**
1. `detection_outcome` (`!= suppressed_alert` → 100% `evasion_attempt`)
2. `detector_confidence_score` (threshold-derived from `detection_outcome`)
3. `evasion_budget_consumed` (`== 0` → 100% one of 3 early phases)

**Other documented leaks (for transparency):**
4. `stealth_score` near-deterministic per `attacker_capability_tier` (inflates per-campaign tier prediction from honest ~0.50 to 0.94)
5. Topology fingerprint — 7 segment-level features uniquely identify each `defender_architecture` (makes 8-class defender prediction trivially 100%, collapses to 0.13 when fingerprint dropped)
6. `timestep` partial oracle for 3 phases — **KEPT in the published model as legitimate campaign-progress observable**

**7 phases in data, README claims 6:** The data adds `idle_dwell` as
a phase (17.5% of events). The published baseline trains on all 7.

**4 README-suggested headline targets unlearnable after honest leak
removal:**
- `campaign_success_flag` binary (acc 0.51 vs majority 0.61)
- `campaign_type` 8-class (acc 0.11 vs majority 0.17)
- `coordinated_attack_flag` binary (acc 0.83 vs majority 0.90, only 20 positives)
- `defender_architecture` 8-class (collapses when topology fingerprint dropped)

**Only viable headline target:** `attack_phase` 7-class — acc 0.867,
ROC-AUC 0.977 with group-aware split. All 7 classes earn nonzero F1
(range 0.49-1.00).

## Calibrated Benchmark Targets

The full product is calibrated to 12 benchmark validation tests drawn from
authoritative adversarial ML research (MITRE ATLAS, NIST AI 100-2 Adversarial
ML Taxonomy, OWASP ML Top 10, USENIX Security adversarial ML papers, IEEE
SaTML, Microsoft Counterfit, IBM Adversarial Robustness Toolbox, Anthropic /
OpenAI red team reports).

Sample benchmark results:

| Test | Target | Observed | Verdict |
|------|--------|----------|---------|
| evasion_success_rate_apt | 0.1430 | 0.1764 | ✓ PASS |
| detection_rate_ensemble | 0.9100 | 0.9100 | ✓ PASS |
| alert_suppression_rate | 0.0720 | 0.0720 | ✓ PASS |
| perturbation_budget_mean | 0.1850 | 0.1891 | ✓ PASS |
| query_volume_rate | 0.1450 | 0.1250 | ✓ PASS |
| concept_drift_injection_rate | 0.0800 | 0.0600 | ✓ PASS |
| stealth_score_apt | 0.7200 | 0.7200 | ✓ PASS |
| retrain_trigger_rate | 0.1400 | 0.1250 | ✓ PASS |
| campaign_success_rate | 0.3800 | 0.3950 | ✓ PASS |
| lateral_pivot_rate | 0.0950 | 0.0950 | ✓ PASS |
| transfer_attack_success_rate | 0.3100 | 0.3100 | ✓ PASS |
| attribution_risk_score | 0.2800 | 0.3201 | ✓ PASS |

*Every CYB011 benchmark in the sample lands within the same calibrated
tolerance as the full product. The sample uses 200 campaigns (vs 5,500 at
full scale); APT-tier conditional benchmarks (≈ 22% of campaigns) have
~44 samples for robust convergence.*

## Schema Highlights

### `attack_trajectories.csv` (primary file, per-timestep)

| Column                          | Type    | Description                                  |
|---------------------------------|---------|----------------------------------------------|
| campaign_id                     | string  | Unique adversarial campaign ID               |
| attacker_id                     | string  | Attacker ID                                  |
| timestep                        | int     | Step in 6-phase lifecycle (0–69)             |
| adversarial_phase               | string  | 1 of 6 phases                                |
| attacker_tier                   | string  | script_kiddie / opportunistic / apt / nation_state |
| defender_architecture           | string  | ensemble / gradient_boosted / nn / isolation_forest / etc. |
| segment_id                      | string  | FK to `network_topology.csv`                 |
| perturbation_linf               | float   | L∞ perturbation magnitude (ε)                |
| perturbation_l2                 | float   | L2 perturbation magnitude                    |
| queries_used                    | int     | Cumulative model queries                     |
| query_budget_remaining          | int     | Tier-cap minus queries_used                  |
| gradient_access                 | int     | Boolean — white-box gradient access          |
| evasion_attempted               | int     | Boolean — evasion submitted at this step     |
| evasion_succeeded               | int     | Boolean — evasion bypassed detection         |
| defender_detection_strength     | float   | Per-architecture detection strength (0–1)    |
| concept_drift_injected          | int     | Boolean — drift injection at this step       |
| transfer_attack_used            | int     | Boolean — perturbation from surrogate model  |
| stealth_score                   | float   | Cumulative stealth (0–1)                     |
| feature_space_dim               | int     | Target model feature dimensionality          |

### `campaign_summary.csv` (per-campaign outcome)

| Column                          | Type    | Description                                  |
|---------------------------------|---------|----------------------------------------------|
| campaign_id, attacker_id        | string  | Identifiers                                  |
| attacker_tier                   | string  | Tier classification target                   |
| defender_architecture           | string  | Defender model classification target         |
| campaign_outcome                | string  | success / detected / aborted / blocked       |
| evasion_success_flag            | int     | Boolean — evasion ever succeeded             |
| total_queries_used              | int     | Cumulative query count                       |
| perturbation_budget_mean        | float   | Mean ε across campaign                       |
| concept_drift_injected_flag     | int     | Boolean — drift injection used               |
| retrain_triggered_flag          | int     | Boolean — defender retraining triggered      |
| transfer_attack_success_flag    | int     | Boolean — transfer attack succeeded          |
| lateral_pivot_flag              | int     | Boolean — pivot to second model              |
| stealth_score_final             | float   | Final stealth score                          |
| attribution_risk_score          | float   | Likelihood of attribution (0–1)              |

See `campaign_events.csv` and `network_topology.csv` for the discrete event
log and segment/defender registry schemas respectively.

## Suggested Use Cases

- Training **adversarial example detectors** — distinguish clean vs
  perturbed inputs from feature-space telemetry
- **Attack phase classification** (the baseline ships this) — predict
  the 7-phase position of a trajectory event —
  [worked example available](https://huggingface.co/xpertsystems/cyb011-baseline-classifier)
- **Attacker tier attribution** — 4-class classification of evasion
  campaigns by capability tier (see leakage diagnostic — nation_state
  tier MISSING from sample; per-campaign prediction inflated by
  stealth_score leakage)
- **Defender architecture vulnerability assessment** — predict which
  defender architectures are most evadable (see leakage diagnostic —
  trivially leaky via topology fingerprint; unlearnable when dropped)
- **L∞ / L2 perturbation budget detection** — calibrate ε-thresholds
- **Query budget exhaustion attacks** — model black-box query patterns
- **Concept drift poisoning detection** — distinguish natural drift
  from adversarial injection
- **Transfer attack detection** — identify perturbations crafted on
  surrogate models
- **MLOps adversarial robustness benchmarking** — evaluate model
  hardening before deployment
- **Honeypot effectiveness analysis** — deception model coverage tuning
- **Adversarial ML threat modeling** — MITRE ATLAS tactic coverage
- **Anthropic / OpenAI-style red team simulation** — synthetic
  jailbreak/evasion training data

## Loading the Data

```python
import pandas as pd

trajectories = pd.read_csv("attack_trajectories.csv")
summaries    = pd.read_csv("campaign_summary.csv")
events       = pd.read_csv("campaign_events.csv")
topology     = pd.read_csv("network_topology.csv")

# Join trajectory data with topology (segment-level features)
enriched = trajectories.merge(topology, left_on="target_segment_id",
                              right_on="segment_id", how="left")

# 7-class attack_phase target (the baseline ships this)
y_phase = trajectories["attack_phase"]

# Multi-class attacker tier (3 values in sample; see leakage diagnostic)
y_tier = trajectories["attacker_capability_tier"]

# Binary evasion success target (see leakage diagnostic — unlearnable)
y_evasion = summaries["campaign_success_flag"]

# Multi-class defender architecture target (see leakage diagnostic —
# trivially leaky via topology fingerprint)
y_defender = topology["defender_architecture"]
```

For a worked end-to-end example with `attack_phase` 7-class
classification, group-aware splitting, feature engineering, and the
full 6-oracle-path leakage audit, see the
[baseline classifier repo](https://huggingface.co/xpertsystems/cyb011-baseline-classifier).

## License

This **sample** is released under **CC-BY-NC-4.0** (free for non-commercial
research and evaluation). The **full production dataset** is licensed
commercially — contact XpertSystems.ai for licensing terms.

## Full Product

The full CYB011 dataset includes **~383,000 rows** across all four files,
with calibrated benchmark validation against 12 metrics drawn from
authoritative adversarial ML research sources (MITRE ATLAS, NIST AI 100-2,
OWASP ML Top 10).

📧  **pradeep@xpertsystems.ai**
🌐  **https://xpertsystems.ai**

## Citation

```bibtex
@dataset{xpertsystems_cyb011_sample_2026,
  title  = {CYB011: Synthetic AI Evasion Attack Trajectory Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/cyb011-sample}
}
```

## Generation Details

- Generator version : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-16 14:56:19 UTC
- Adversarial model : 6-phase evasion campaign state machine
- Overall benchmark : 100.0 / 100  (grade A+)