| ---
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| license: cc-by-nc-4.0
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| task_categories:
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| - tabular-classification
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| - time-series-forecasting
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| tags:
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| - cybersecurity
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| - insider-threat
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| - ueba
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| - data-exfiltration
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| - synthetic-data
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| - privileged-access
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| - hr-analytics
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| - dlp
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| - zero-trust
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| - behavioral-analytics
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| pretty_name: CYB007 — Synthetic Insider Threat Dataset (Sample)
|
| size_categories:
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| - 10K<n<100K
|
| ---
|
|
|
| # CYB007 — Synthetic Insider Threat Dataset (Sample)
|
|
|
| **XpertSystems.ai Synthetic Data Platform · SKU: CYB007-SAMPLE · Version 1.0.0**
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|
|
| This is a **free preview** of the full **CYB007 — Synthetic Insider Threat
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| Dataset** product. It contains roughly **~10% of the full dataset** at
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| identical schema, actor-tier distribution, and statistical fingerprint, so
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| you can evaluate fit before licensing the full product.
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|
|
| > 🤖 **Trained baseline available:**
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| > [**xpertsystems/cyb007-baseline-classifier**](https://huggingface.co/xpertsystems/cyb007-baseline-classifier)
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| > — XGBoost + PyTorch MLP for **3-tier insider threat type classification**
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| > (the README's stated headline use case), group-aware split by incident,
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| > multi-seed evaluation (ROC-AUC 0.961 ± 0.007), honest leakage audit of
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| > tier-correlated volume features.
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|
|
| | File | Rows (sample) | Rows (full) | Description |
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| |-------------------------------|---------------|---------------|----------------------------------------------|
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| | `org_topology.csv` | ~240 | ~2,400 | Department / org structure registry |
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| | `incident_summary.csv` | ~500 | ~4,800 | Per-incident aggregate outcomes |
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| | `incident_events.csv` | ~38,687 | ~48,000 | Discrete incident event log |
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| | `insider_trajectories.csv` | ~32,500 | ~280,000 | Per-timestep trajectory data (primary file) |
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|
|
| ## Dataset Summary
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|
|
| CYB007 simulates end-to-end insider threat incident lifecycles as a **6-phase
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| state machine** across enterprise org topologies with calibrated UEBA
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| defender modeling, covering:
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|
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| - **4 actor threat-type tiers**: negligent_user, malicious_employee,
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| privileged_insider, compromised_account — with per-tier stealth weights,
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| data access scopes, cover-tracks propensity, and collusion probabilities
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| - **8 UEBA defender statuses** (graduated maturity ladder): no_ueba,
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| dlp_only, siem_only, partial_coverage, pam_integrated, hr_integrated,
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| full_coverage, zero_trust_enforced — each with distinct detection_strength
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| and alert_suppression characteristics
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| - **6 lifecycle phases**: reconnaissance, access_escalation, data_staging,
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| exfiltration_attempt, cover_tracks, incident_resolution
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| - **Exfiltration channels**: email, USB, cloud upload, print, screen capture
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| - **HR-trigger modeling** — disgruntlement signals, performance reviews,
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| resignation indicators, and behavioural anomalies that flag IR
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| - **Collusion modeling** — coordinated multi-actor incidents with weighted
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| per-tier collusion probabilities
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| - **Attribution risk scoring** — recon intensity × stealth weight
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| - **Sabotage outcomes** — destructive insider actions distinct from
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| exfiltration
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|
|
| ## Trained Baseline Available
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|
|
| A working baseline classifier trained on this sample is published at
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| **[xpertsystems/cyb007-baseline-classifier](https://huggingface.co/xpertsystems/cyb007-baseline-classifier)**.
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|
|
| | Component | Detail |
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| |---|---|
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| | Task | **3-class insider threat type classification** (the README's headline use case) |
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| | Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
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| | Features | 28 (after one-hot encoding); pipeline included as `feature_engineering.py` |
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| | Split | **Group-aware by incident_id** — train/val/test incidents disjoint |
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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 |
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| | 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) |
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|
|
| This is the **second XpertSystems baseline to ship the dataset's stated
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| headline use case** (after CYB005). CYB007's 500-incident sample is large
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| enough that tier attribution learns honestly under group-aware splitting,
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| with no oracle features and very tight multi-seed std.
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|
|
| **Important schema note for buyers:** the dataset README documents a
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| 4-tier scheme including `compromised_account`, but the sample contains
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| only 3 of those 4 tiers. The baseline trains on what exists in the
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| sample. See the baseline model card for the full list of schema
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| discrepancies between README and data.
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|
|
| ## Calibrated Benchmark Targets
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|
|
| The full product is calibrated to 12 benchmark validation tests drawn from
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| authoritative insider threat research (CERT Insider Threat Center, Verizon
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| DBIR, IBM Cost of Insider Threats, Ponemon Institute, MITRE ATT&CK, NIST
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| SP 800-53 / SP 800-207, Securonix, Forrester UEBA, Gartner ZTNA,
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| CrowdStrike, Mandiant M-Trends).
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|
|
| Sample benchmark results:
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|
|
| | Test | Target | Observed | Verdict |
|
| |------|--------|----------|---------|
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| | exfiltration_success_rate_priv | 0.1460 | 0.1524 | ✓ PASS |
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| | detection_rate_zero_trust | 0.9100 | 0.9100 | ✓ PASS |
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| | alert_suppression_rate | 0.0770 | 0.0798 | ✓ PASS |
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| | data_access_volume_mean_mb | 220.0 | 151.5 | ~ MARGINAL |
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| | cover_tracks_rate | 0.1800 | 0.1720 | ✓ PASS |
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| | dwell_time_ratio | 0.2200 | 0.2212 | ✓ PASS |
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| | stealth_score_privileged | 0.6800 | 0.7047 | ✓ PASS |
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| | hr_trigger_rate | 0.1600 | 0.1220 | ✓ PASS |
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| | incident_success_rate | 0.3400 | 0.3500 | ✓ PASS |
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| | lateral_access_rate | 0.1100 | 0.1092 | ✓ PASS |
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| | collusion_rate | 0.0850 | 0.0420 | ~ MARGINAL |
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| | attribution_risk_score | 0.3100 | 0.2746 | ✓ PASS |
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|
|
| *Note: some benchmarks (e.g. privileged_insider exfil success rate,
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| collusion rate, attribution risk) are conditional on smaller actor-tier
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| subsets. The full product (4,800 incidents) demonstrates all 12 benchmarks
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| at Grade A- or better with strong statistical power.*
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|
|
| ## Schema Highlights
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|
|
| ### `insider_trajectories.csv` (primary file, per-timestep)
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|
|
| | Column | Type | Description |
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| |---------------------------------|---------|----------------------------------------------|
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| | incident_id | string | Unique incident identifier |
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| | actor_id | string | Insider actor ID |
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| | timestep | int | Step in 6-phase lifecycle (0–64) |
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| | phase | string | 1 of 6 phases |
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| | data_access_volume_mb | float | Per-step data accessed |
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| | payload_entropy | float | Data payload entropy (0–8) |
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| | cover_actions_taken | int | Cover-tracks actions at this step |
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| | dlp_alerts_raised | int | DLP alerts triggered |
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| | detection_flag | int | Boolean — UEBA detection at this step |
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| | exfil_cumulative_mb | float | Cumulative exfiltrated data |
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| | blast_radius | float | Org-wide compromise score |
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| | sensitive_data_accessed | int | Boolean — sensitive data touched |
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| | threat_type_tier | string | negligent_user / malicious_employee / privileged_insider / compromised_account |
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|
|
| ### `incident_summary.csv` (per-incident outcome)
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|
|
| | Column | Type | Description |
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| |---------------------------------|---------|----------------------------------------------|
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| | incident_id, actor_id | string | Identifiers |
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| | threat_type_tier | string | Tier classification target |
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| | ueba_status | string | Defender maturity tier |
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| | incident_success_flag | int | Boolean — incident succeeded |
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| | exfiltration_success_flag | int | Boolean — data exfiltrated |
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| | total_data_volume_mb | float | Total accessed in incident |
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| | exfiltrated_volume_mb | float | Total exfiltrated |
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| | cover_tracks_flag | int | Boolean — log tampering attempted |
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| | hr_trigger_flag | int | Boolean — HR-detected indicators |
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| | stealth_score | float | Overall stealth (0–1) |
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| | dwell_time_ratio | float | Fraction of timesteps in dwell |
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| | collusion_flag | int | Boolean — multi-actor coordination |
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| | attribution_risk_score | float | Likelihood of attribution (0–1) |
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| | lateral_access_flag | int | Boolean — out-of-scope dept access |
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| | sabotage_flag | int | Boolean — destructive action |
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|
|
| See `incident_events.csv` and `org_topology.csv` for the discrete event log
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| 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](https://huggingface.co/xpertsystems/cyb007-baseline-classifier)
|
| - **Data exfiltration detection** modelling — DLP signal calibration
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| - **UEBA effectiveness benchmarking** — graduated 8-tier defender maturity
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| - **HR-signal correlation** — disgruntlement, resignation, performance
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| triggers for early-warning systems
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| - **Cover-tracks / log-tampering detection** — stealth feature engineering
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| - **Privileged access misuse** detection (privileged_insider tier)
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| - **Collusion detection** — multi-actor coordinated incident patterns
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| - **Attribution risk modelling** — recon intensity × stealth
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| - **Zero Trust posture validation** — block rates by defender maturity tier
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| - **Sabotage vs exfiltration** discrimination
|
|
|
| ## Loading the Data
|
|
|
| ```python
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| import pandas as pd
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|
|
| trajectories = pd.read_csv("insider_trajectories.csv")
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| incidents = pd.read_csv("incident_summary.csv")
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| events = pd.read_csv("incident_events.csv")
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| topology = pd.read_csv("org_topology.csv")
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|
|
| # Join trajectory data with incident-level labels
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| enriched = trajectories.merge(incidents, on=["incident_id", "actor_id"],
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| how="left", suffixes=("", "_summary"))
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|
|
| # 3-class threat-type classification target (sample contains 3 of 4 README tiers)
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| y_tier = trajectories["actor_threat_type"]
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|
|
| # Binary exfiltration-success target
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| y_exfil = incidents["exfiltration_successes"] > 0
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|
|
| # Binary coordinated-incident target
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| y_coord = incidents["coordinated_incident_flag"]
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| ```
|
|
|
| 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](https://huggingface.co/xpertsystems/cyb007-baseline-classifier/blob/main/inference_example.ipynb).
|
|
|
| ## License
|
|
|
| This **sample** is released under **CC-BY-NC-4.0** (free for non-commercial
|
| research and evaluation). The **full production dataset** is licensed
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| 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
|
|
|
| ```bibtex
|
| @dataset{xpertsystems_cyb007_sample_2026,
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| title = {CYB007: Synthetic Insider Threat Dataset (Sample)},
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| author = {XpertSystems.ai},
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| year = {2026},
|
| url = {https://huggingface.co/datasets/xpertsystems/cyb007-sample}
|
| }
|
| ```
|
|
|
| ## Generation Details
|
|
|
| - Generator version : 1.0.0
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| - Random seed : 42
|
| - Generated : 2026-05-16 14:17:56 UTC
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| - Lifecycle model : 6-phase insider threat state machine
|
| - Overall benchmark : 95.3 / 100 (grade A)
|
|
|
