| ---
|
| license: cc-by-nc-4.0
|
| task_categories:
|
| - tabular-classification
|
| - time-series-forecasting
|
| tags:
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| - cybersecurity
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| - adversarial-machine-learning
|
| - ai-security
|
| - adversarial-attacks
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| - evasion-attacks
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| - apt
|
| - synthetic-data
|
| - ml-security
|
| - model-robustness
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| - mlops-security
|
| pretty_name: CYB011 — Synthetic AI Evasion Attack Trajectories (Sample)
|
| size_categories:
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| - 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)
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| > — XGBoost + PyTorch MLP for **7-class adversarial attack phase
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| > classification** (the dataset's headline target), group-aware split
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| > by `campaign_id`, multi-seed evaluation (acc 0.867 ± 0.010, ROC-AUC
|
| > 0.977 ± 0.002). **Includes a comprehensive `leakage_diagnostic.json`**
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| > documenting 6 oracle paths discovered across the dataset's targets,
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| > 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 |
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| | `campaign_events.csv` | ~13,310 | ~55,000 | Discrete campaign event log |
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| | `attack_trajectories.csv` | ~14,000 | ~320,000 | Per-timestep adversarial trajectories |
|
|
|
| ## Dataset Summary
|
|
|
| CYB011 simulates end-to-end **adversarial AI evasion attack campaigns**
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| against ML-based security detection systems, modeled as a **6-phase
|
| adversarial state machine** (data has 7 phases — adds `idle_dwell`):
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|
|
| - **6 adversarial phases**: reconnaissance → feature_space_probe →
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| perturbation_craft → evasion_attempt → feedback_adaptation →
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| campaign_consolidation
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| - **4 attacker capability tiers**: script_kiddie, opportunistic,
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| advanced_persistent_threat (APT), nation_state — with per-tier
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| ε-budgets (L∞ perturbation), query budgets (50 → 5,000), base evasion
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| rates, and stealth weights
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| - **8 defender detection architectures** with per-architecture
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| detection_strength (e.g. ensemble_layered 0.91, gradient_boosted 0.78,
|
| neural_network 0.74, isolation_forest 0.62)
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| - **L∞ perturbation budget modeling** — calibrated mean ε ≈ 0.185
|
| representing realistic imperceptibility constraints
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| - **Query budget tracking** — black-box vs white-box attack distinction
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| - **Concept drift injection** — adversarial data poisoning of training
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| distributions, ~8% injection rate
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| - **Retraining trigger modeling** — defender model refresh after drift
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| detection (~14% trigger rate)
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| - **Transfer attack modeling** — perturbations crafted on surrogate
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| models, 31% transfer success rate
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| - **Honeypot density** — deception model coverage (5% baseline)
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| - **Coordinated multi-attacker campaigns** with 12% coordination rate
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| - **MLOps security signals** — gradient access patterns, feature-space
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| probing, lateral pivoting between models
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|
|
| ## 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`) |
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| | 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 |
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| | Validation | Single seed + multi-seed aggregate across 10 seeds |
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| | 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%).
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| 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):**
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| 1. `detection_outcome` (`!= suppressed_alert` → 100% `evasion_attempt`)
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| 2. `detector_confidence_score` (threshold-derived from `detection_outcome`)
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| 3. `evasion_budget_consumed` (`== 0` → 100% one of 3 early phases)
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|
|
| **Other documented leaks (for transparency):**
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| 4. `stealth_score` near-deterministic per `attacker_capability_tier` (inflates per-campaign tier prediction from honest ~0.50 to 0.94)
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| 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)
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| - `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 |
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| | 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 |
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| | attacker_id | string | Attacker ID |
|
| | timestep | int | Step in 6-phase lifecycle (0–69) |
|
| | adversarial_phase | string | 1 of 6 phases |
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| | attacker_tier | string | script_kiddie / opportunistic / apt / nation_state |
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| | defender_architecture | string | ensemble / gradient_boosted / nn / isolation_forest / etc. |
|
| | segment_id | string | FK to `network_topology.csv` |
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| | perturbation_linf | float | L∞ perturbation magnitude (ε) |
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| | perturbation_l2 | float | L2 perturbation magnitude |
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| | queries_used | int | Cumulative model queries |
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| | query_budget_remaining | int | Tier-cap minus queries_used |
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| | gradient_access | int | Boolean — white-box gradient access |
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| | evasion_attempted | int | Boolean — evasion submitted at this step |
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| | evasion_succeeded | int | Boolean — evasion bypassed detection |
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| | 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 |
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| | stealth_score | float | Cumulative stealth (0–1) |
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| | feature_space_dim | int | Target model feature dimensionality |
|
|
|
| ### `campaign_summary.csv` (per-campaign outcome)
|
|
|
| | Column | Type | Description |
|
| |---------------------------------|---------|----------------------------------------------|
|
| | campaign_id, attacker_id | string | Identifiers |
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| | attacker_tier | string | Tier classification target |
|
| | defender_architecture | string | Defender model classification target |
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| | campaign_outcome | string | success / detected / aborted / blocked |
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| | evasion_success_flag | int | Boolean — evasion ever succeeded |
|
| | total_queries_used | int | Cumulative query count |
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| | perturbation_budget_mean | float | Mean ε across campaign |
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| | concept_drift_injected_flag | int | Boolean — drift injection used |
|
| | retrain_triggered_flag | int | Boolean — defender retraining triggered |
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| | transfer_attack_success_flag | int | Boolean — transfer attack succeeded |
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| | lateral_pivot_flag | int | Boolean — pivot to second model |
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| | stealth_score_final | float | Final stealth score |
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| | 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+)
|
|
|
