xpertsystems/cyb011-sample

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.

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:

• 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

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
• Attacker tier attribution — 4-class classification of evasion campaigns by capability tier
• Defender architecture vulnerability assessment — predict which defender architectures are most evadable
• 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

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 campaign-level labels
enriched = trajectories.merge(summaries, on=["campaign_id", "attacker_id"],
                              how="left", suffixes=("", "_summary"))
enriched = enriched.merge(topology, on="segment_id", how="left")

# 4-class attacker tier target
y_tier = summaries["attacker_tier"]

# Binary evasion success target
y_evasion = summaries["evasion_success_flag"]

# Multi-class defender architecture target
y_defender = topology["defender_architecture"]

# Binary concept drift / poisoning detection
y_poisoned = summaries["concept_drift_injected_flag"]

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

@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+)