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cyb007-sample

Tasks:
Tabular Classification
Time Series Forecasting
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Tags:
cybersecurity
insider-threat
ueba
data-exfiltration
synthetic-data
privileged-access
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metadata 
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - cybersecurity
  - insider-threat
  - ueba
  - data-exfiltration
  - synthetic-data
  - privileged-access
  - hr-analytics
  - dlp
  - zero-trust
  - behavioral-analytics
pretty_name: CYB007 — Synthetic Insider Threat Dataset (Sample)
size_categories:
  - 10K<n<100K

CYB007 — Synthetic Insider Threat Dataset (Sample)

XpertSystems.ai Synthetic Data Platform · SKU: CYB007-SAMPLE · Version 1.0.0

This is a free preview of the full CYB007 — Synthetic Insider Threat Dataset product. It contains roughly ~10% of the full dataset at identical schema, actor-tier distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.

🤖 Trained baseline available: xpertsystems/cyb007-baseline-classifier — XGBoost + PyTorch MLP for 3-tier insider threat type classification (the README's stated headline use case), group-aware split by incident, multi-seed evaluation (ROC-AUC 0.961 ± 0.007), honest leakage audit of tier-correlated volume features.

File Rows (sample) Rows (full) Description
org_topology.csv ~240 ~2,400 Department / org structure registry
incident_summary.csv ~500 ~4,800 Per-incident aggregate outcomes
incident_events.csv ~38,687 ~48,000 Discrete incident event log
insider_trajectories.csv ~32,500 ~280,000 Per-timestep trajectory data (primary file)

Dataset Summary

CYB007 simulates end-to-end insider threat incident lifecycles as a 6-phase state machine across enterprise org topologies with calibrated UEBA defender modeling, covering:

  • 4 actor threat-type tiers: negligent_user, malicious_employee, privileged_insider, compromised_account — with per-tier stealth weights, data access scopes, cover-tracks propensity, and collusion probabilities
  • 8 UEBA defender statuses (graduated maturity ladder): no_ueba, dlp_only, siem_only, partial_coverage, pam_integrated, hr_integrated, full_coverage, zero_trust_enforced — each with distinct detection_strength and alert_suppression characteristics
  • 6 lifecycle phases: reconnaissance, access_escalation, data_staging, exfiltration_attempt, cover_tracks, incident_resolution
  • Exfiltration channels: email, USB, cloud upload, print, screen capture
  • HR-trigger modeling — disgruntlement signals, performance reviews, resignation indicators, and behavioural anomalies that flag IR
  • Collusion modeling — coordinated multi-actor incidents with weighted per-tier collusion probabilities
  • Attribution risk scoring — recon intensity × stealth weight
  • Sabotage outcomes — destructive insider actions distinct from exfiltration

Trained Baseline Available

A working baseline classifier trained on this sample is published at xpertsystems/cyb007-baseline-classifier.

Component Detail
Task 3-class insider threat type classification (the README's headline use case)
Models XGBoost (model_xgb.json) + PyTorch MLP (model_mlp.safetensors)
Features 28 (after one-hot encoding); pipeline included as feature_engineering.py
Split Group-aware by incident_id — train/val/test incidents disjoint
Validation Single seed + multi-seed aggregate across 10 seeds
Demo inference_example.ipynb — end-to-end copy-paste
Headline metrics XGBoost: accuracy 0.855 ± 0.012, macro ROC-AUC 0.961 ± 0.007 (multi-seed); MLP slightly outperforms (acc 0.869, AUC 0.966 at seed 42)

This is the second XpertSystems baseline to ship the dataset's stated headline use case (after CYB005). CYB007's 500-incident sample is large enough that tier attribution learns honestly under group-aware splitting, with no oracle features and very tight multi-seed std.

Important schema note for buyers: the dataset README documents a 4-tier scheme including compromised_account, but the sample contains only 3 of those 4 tiers. The baseline trains on what exists in the sample. See the baseline model card for the full list of schema discrepancies between README and data.

Calibrated Benchmark Targets

The full product is calibrated to 12 benchmark validation tests drawn from authoritative insider threat research (CERT Insider Threat Center, Verizon DBIR, IBM Cost of Insider Threats, Ponemon Institute, MITRE ATT&CK, NIST SP 800-53 / SP 800-207, Securonix, Forrester UEBA, Gartner ZTNA, CrowdStrike, Mandiant M-Trends).

Sample benchmark results:

Test Target Observed Verdict
exfiltration_success_rate_priv 0.1460 0.1524 ✓ PASS
detection_rate_zero_trust 0.9100 0.9100 ✓ PASS
alert_suppression_rate 0.0770 0.0798 ✓ PASS
data_access_volume_mean_mb 220.0 151.5 ~ MARGINAL
cover_tracks_rate 0.1800 0.1720 ✓ PASS
dwell_time_ratio 0.2200 0.2212 ✓ PASS
stealth_score_privileged 0.6800 0.7047 ✓ PASS
hr_trigger_rate 0.1600 0.1220 ✓ PASS
incident_success_rate 0.3400 0.3500 ✓ PASS
lateral_access_rate 0.1100 0.1092 ✓ PASS
collusion_rate 0.0850 0.0420 ~ MARGINAL
attribution_risk_score 0.3100 0.2746 ✓ PASS

Note: some benchmarks (e.g. privileged_insider exfil success rate, collusion rate, attribution risk) are conditional on smaller actor-tier subsets. The full product (4,800 incidents) demonstrates all 12 benchmarks at Grade A- or better with strong statistical power.

Schema Highlights

insider_trajectories.csv (primary file, per-timestep)

Column Type Description
incident_id string Unique incident identifier
actor_id string Insider actor ID
timestep int Step in 6-phase lifecycle (0–64)
phase string 1 of 6 phases
data_access_volume_mb float Per-step data accessed
payload_entropy float Data payload entropy (0–8)
cover_actions_taken int Cover-tracks actions at this step
dlp_alerts_raised int DLP alerts triggered
detection_flag int Boolean — UEBA detection at this step
exfil_cumulative_mb float Cumulative exfiltrated data
blast_radius float Org-wide compromise score
sensitive_data_accessed int Boolean — sensitive data touched
threat_type_tier string negligent_user / malicious_employee / privileged_insider / compromised_account

incident_summary.csv (per-incident outcome)

Column Type Description
incident_id, actor_id string Identifiers
threat_type_tier string Tier classification target
ueba_status string Defender maturity tier
incident_success_flag int Boolean — incident succeeded
exfiltration_success_flag int Boolean — data exfiltrated
total_data_volume_mb float Total accessed in incident
exfiltrated_volume_mb float Total exfiltrated
cover_tracks_flag int Boolean — log tampering attempted
hr_trigger_flag int Boolean — HR-detected indicators
stealth_score float Overall stealth (0–1)
dwell_time_ratio float Fraction of timesteps in dwell
collusion_flag int Boolean — multi-actor coordination
attribution_risk_score float Likelihood of attribution (0–1)
lateral_access_flag int Boolean — out-of-scope dept access
sabotage_flag int Boolean — destructive action

See incident_events.csv and org_topology.csv for the discrete event log and department registry schemas respectively.

Suggested Use Cases

  • Training insider threat classifier models (3-tier actor attribution on the sample, 4-tier on the full product) — worked example available
  • Data exfiltration detection modelling — DLP signal calibration
  • UEBA effectiveness benchmarking — graduated 8-tier defender maturity
  • HR-signal correlation — disgruntlement, resignation, performance triggers for early-warning systems
  • Cover-tracks / log-tampering detection — stealth feature engineering
  • Privileged access misuse detection (privileged_insider tier)
  • Collusion detection — multi-actor coordinated incident patterns
  • Attribution risk modelling — recon intensity × stealth
  • Zero Trust posture validation — block rates by defender maturity tier
  • Sabotage vs exfiltration discrimination

Loading the Data 

import pandas as pd

trajectories = pd.read_csv("insider_trajectories.csv")
incidents    = pd.read_csv("incident_summary.csv")
events       = pd.read_csv("incident_events.csv")
topology     = pd.read_csv("org_topology.csv")

# Join trajectory data with incident-level labels
enriched = trajectories.merge(incidents, on=["incident_id", "actor_id"],
                              how="left", suffixes=("", "_summary"))

# 3-class threat-type classification target (sample contains 3 of 4 README tiers)
y_tier = trajectories["actor_threat_type"]

# Binary exfiltration-success target
y_exfil = incidents["exfiltration_successes"] > 0

# Binary coordinated-incident target
y_coord = incidents["coordinated_incident_flag"]

For a worked end-to-end example with insider-threat tier classification, group-aware splitting, and feature engineering, see the inference notebook in the baseline classifier repo.

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 CYB007 dataset includes ~335,000 rows across all four files, with calibrated benchmark validation against 12 metrics drawn from authoritative insider threat research sources.

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

Citation 

@dataset{xpertsystems_cyb007_sample_2026,
  title  = {CYB007: Synthetic Insider Threat Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/cyb007-sample}
}

Generation Details

  • Generator version : 1.0.0
  • Random seed : 42
  • Generated : 2026-05-16 14:17:56 UTC
  • Lifecycle model : 6-phase insider threat state machine
  • Overall benchmark : 95.3 / 100 (grade A)