Datasets:

xpertsystems
/

cyb007-sample

---
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**](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/xpertsystems/cyb007-baseline-classifier)
- **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

```python
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](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
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,
  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)