--- 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 🤖 **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+)