README.md

---
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - cybersecurity
  - soc-operations
  - alert-triage
  - mitre-attack
  - soar
  - siem
  - synthetic-data
  - incident-response
  - analyst-fatigue
  - false-positive-reduction
pretty_name: CYB008 — Synthetic SOC Alert Dataset (Sample)
size_categories:
  - 10K<n<100K
---

# CYB008 — Synthetic SOC Alert Dataset (Sample)

**XpertSystems.ai Synthetic Data Platform · SKU: CYB008-SAMPLE · Version 1.0.0**

This is a **free preview** of the full **CYB008 — Synthetic SOC Alert
Dataset** product. It contains roughly **~10% of the full dataset** at
identical schema, MITRE ATT&CK tactic coverage, and statistical fingerprint,
so you can evaluate fit before licensing the full product.

> 🤖 **Trained baseline available:**
> [**xpertsystems/cyb008-baseline-classifier**](https://huggingface.co/xpertsystems/cyb008-baseline-classifier)
> — XGBoost + PyTorch MLP for **5-class SOC alert triage outcome
> classification** (the README's first headline use case), stratified
> split, multi-seed evaluation (ROC-AUC 0.955 ± 0.003). **Includes a
> structural-leakage diagnostic** documenting three oracle columns
> dropped from the feature set, and a separate unlearnable-target
> finding for MITRE ATT&CK tactic classification. Buyers planning
> SOC ML work should read the diagnostic first.

| File                       | Rows (sample) | Rows (full)   | Description                                  |
|----------------------------|---------------|---------------|----------------------------------------------|
| `soc_topology.csv`         | ~25           | ~2,400        | SOC / analyst registry                       |
| `incident_summary.csv`     | ~589           | ~4,800        | Per-incident aggregate outcomes              |
| `alert_events.csv`         | ~55,298         | ~48,000       | Discrete alert event log                     |
| `soc_alerts.csv`           | ~9,200        | ~280,000      | Per-alert records (primary file)             |

## Dataset Summary

CYB008 simulates end-to-end Security Operations Centre (SOC) alert
lifecycles across enterprise detection environments, with:

- **Full MITRE ATT&CK tactic coverage** — alerts mapped to all 14
  Enterprise tactics from reconnaissance through impact
- **Alert severity distribution** — info / low / medium / high / critical
  / false_positive, with calibrated 45% false-positive baseline
- **SOC analyst tier modeling** — tier_1 / tier_2 / tier_3 / SOC manager
  with differentiated MTTR by experience level
- **SOAR automation** — playbook trigger probability, auto-resolution
  rate, automation coverage by alert type
- **Alert storm events** — Poisson-distributed alert bursts (2.5×–6×
  amplification) simulating coordinated attacks or system failures
- **Analyst fatigue modeling** — utilization-driven burnout with MTTR
  degradation past fatigue threshold (0.82)
- **Kill-chain correlated incidents** — alerts grouped into multi-stage
  incidents when ≥3 ATT&CK tactics observed
- **SLA tracking** — severity-dependent SLA thresholds (critical 60min,
  high 240min, medium 480min, low 1440min)
- **Detection source mix** — EDR, SIEM, NDR, IDS, UEBA, CASB, deception,
  threat intel feeds
- **Rule drift modeling** — periodic rule-noise amplification simulating
  detection-engineering signal decay

## Trained Baseline Available

A working baseline classifier trained on this sample is published at
**[xpertsystems/cyb008-baseline-classifier](https://huggingface.co/xpertsystems/cyb008-baseline-classifier)**.

| Component | Detail |
|---|---|
| Primary task | **5-class `resolution_outcome` classification** (SOC alert triage — the README's first headline use case) |
| Diagnostic | Structural-leakage audit (3 oracle columns dropped) + unlearnable-target finding for `mitre_tactic` |
| Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
| Features | 53 (after one-hot encoding); pipeline included as `feature_engineering.py` |
| Split | **Stratified random** — no natural row-level group key (25 analysts, 5 SOCs, only 9% of alerts link to an incident) |
| Validation | Single seed + multi-seed aggregate across 10 seeds |
| Demo | `inference_example.ipynb` — end-to-end copy-paste |
| Headline metrics | XGBoost: accuracy 0.777 ± 0.007, macro ROC-AUC 0.955 ± 0.003 (multi-seed); MLP slightly outperforms |

**Important findings for buyers planning SOC ML work:**

1. **Three structural oracles in the data** (`alert_lifecycle_phase`,
   `automation_resolved`, `escalation_flag`) deterministically encode
   the `resolution_outcome` label. With these columns present, a
   plain XGBoost achieves 100% accuracy. The baseline excludes them
   to demonstrate honest learning — and the documented honest result
   (acc 0.78, AUC 0.96) is genuinely useful.

2. **MITRE ATT&CK tactic classification is NOT learnable on this
   sample.** The README lists tactic classification as a top use case,
   but feature distributions are nearly identical across all 12
   tactics. A trained model performs *below* majority baseline
   (acc 0.08 vs 0.14). The baseline model card documents this
   explicitly with a recommendation to the dataset author.

3. **SLA breach prediction is also not learnable** (acc 0.68 vs
   majority 0.82). Documented as out-of-scope.

See the model card and `leakage_diagnostic.json` for the full audit
and our recommendations to make these tasks viable in the next
dataset version.

## Calibrated Benchmark Targets

The full product is calibrated to 12 benchmark validation tests drawn from
authoritative SOC operations research (SANS SOC Survey, IBM Cost of Data
Breach, Mandiant M-Trends, Forrester Wave SOAR, Gartner SIEM Magic
Quadrant, SOC.OS, CrowdStrike, Splunk State of Security, Verizon DBIR).

Sample benchmark results:

| Test | Target | Observed | Verdict |
|------|--------|----------|---------|
| false_positive_rate | 0.4500 | 0.4518 | ✓ PASS |
| mttd_minutes_mean | 132.0 | 137.1 | ✓ PASS |
| mttr_minutes_mean | 480.0 | 494.9 | ✓ PASS |
| escalation_rate | 0.2200 | 0.2038 | ✓ PASS |
| auto_resolution_rate | 0.3100 | 0.2872 | ✓ PASS |
| alert_volume_rate | 0.1650 | 0.1840 | ✓ PASS |
| analyst_fatigue_score | 0.6400 | 0.6457 | ✓ PASS |
| soar_playbook_execution_rate | 0.4200 | 0.4223 | ✓ PASS |
| incident_declaration_rate | 0.0850 | 0.0640 | ✓ PASS |
| true_positive_rate | 0.3800 | 0.3442 | ✓ PASS |
| kill_chain_completion_rate | 0.1450 | 0.1290 | ✓ PASS |
| sla_breach_rate | 0.1800 | 0.1775 | ✓ PASS |

*Note: every CYB008 benchmark is directly parametrised by the generator
(e.g. `soar_trigger_prob=0.42` produces `soar_playbook_execution_rate=0.42`).
Calibration is precise even at sample scale. The full product produces the
same calibration across 28× more data.*

## Schema Highlights

### `soc_alerts.csv` (primary file)

| Column                          | Type    | Description                                  |
|---------------------------------|---------|----------------------------------------------|
| alert_id                        | string  | Unique alert identifier                      |
| incident_id                     | string  | Parent incident FK (nullable)                |
| soc_id                          | string  | SOC environment FK                           |
| analyst_id                      | string  | Assigned analyst FK                          |
| alert_timestamp                 | string  | ISO timestamp                                |
| alert_severity                  | string  | info / low / medium / high / critical / false_positive |
| mitre_tactic                    | string  | 1 of 14 ATT&CK tactics                       |
| mitre_technique_id              | string  | T-number (e.g. T1059.001)                    |
| detection_source                | string  | edr / siem / ndr / ids / ueba / casb / etc.  |
| triage_score                    | float   | Initial triage priority (0–1)                |
| enrichment_score                | float   | Threat-intel enrichment quality (0–1)        |
| escalation_flag                 | int     | Boolean — escalated to tier 2/3              |
| automation_resolved             | int     | Boolean — SOAR auto-resolved                 |
| soar_playbook_triggered         | int     | Boolean — SOAR playbook executed             |
| mttd_minutes                    | float   | Mean time to detect                          |
| mttr_minutes                    | float   | Mean time to respond                         |
| sla_breached_flag               | int     | Boolean — SLA breached                       |
| resolution_outcome              | string  | true_positive / false_positive / duplicate / suppressed |
| analyst_tier                    | string  | tier_1 / tier_2 / tier_3 / manager           |
| storm_event_flag                | int     | Boolean — part of alert storm                |
| kill_chain_stage                | int     | Position in kill chain (0 if standalone)     |

### `incident_summary.csv` (per-incident outcome)

| Column                          | Type    | Description                                  |
|---------------------------------|---------|----------------------------------------------|
| incident_id                     | string  | Identifier                                   |
| soc_id, analyst_id              | string  | Identifiers                                  |
| n_alerts_correlated             | int     | Alerts grouped into this incident            |
| kill_chain_stages_observed      | int     | Distinct ATT&CK tactics in chain             |
| incident_severity               | string  | Composite severity                           |
| incident_resolution_outcome     | string  | true_positive / false_positive / partial     |
| analyst_fatigue_score           | float   | Avg fatigue during incident (0–1)            |
| incident_duration_minutes       | float   | End-to-end response time                     |

See `alert_events.csv` and `soc_topology.csv` for the discrete event log
and SOC registry schemas respectively.

## Suggested Use Cases

- Training **alert triage** models — predict TP vs FP, or full 5-class
  resolution outcome (the baseline ships this) —
  [worked example available](https://huggingface.co/xpertsystems/cyb008-baseline-classifier)
- **MITRE ATT&CK tactic classification** from alert features (see baseline diagnostic — not learnable on this sample)
- **SOAR playbook recommendation** — predict which alerts benefit from
  automation
- **Alert prioritization** — calibrate triage scores against ground-truth
  outcomes
- **Analyst fatigue forecasting** — predict burnout from shift-level
  workload
- **Kill-chain detection** — group related alerts into multi-stage
  incidents
- **SLA breach prediction** — early-warning systems (see baseline diagnostic — not learnable on this sample)
- **Alert storm detection** — distinguish coordinated bursts from baseline
  volume
- **False positive reduction** modeling — reduce 45% FP rate
- **Detection rule tuning** — identify rules with high noise factor

## Loading the Data

```python
import pandas as pd

alerts    = pd.read_csv("soc_alerts.csv")
incidents = pd.read_csv("incident_summary.csv")
events    = pd.read_csv("alert_events.csv")
topology  = pd.read_csv("soc_topology.csv")

# Join alerts with analyst context
enriched = alerts.merge(topology, on=["soc_id", "analyst_id"], how="left",
                        suffixes=("", "_analyst"))

# 5-class triage outcome target (the README's first headline use case)
y_outcome = alerts["resolution_outcome"]

# Binary true-positive collapse (for binary triage)
y_tp = alerts["resolution_outcome"].isin([
    "true_positive_remediated", "true_positive_escalated",
]).astype(int)

# Multi-class ATT&CK tactic classification target — see leakage diagnostic
y_tactic = alerts["mitre_tactic"]

# Binary SLA breach prediction target — see leakage diagnostic
y_sla = alerts["sla_breached_flag"]
```

For a worked end-to-end example with 5-class triage classification,
stratified splitting, and feature engineering, see the inference notebook
in the [baseline classifier repo](https://huggingface.co/xpertsystems/cyb008-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 CYB008 dataset includes **~335,000 rows** across all four files,
with calibrated benchmark validation against 12 metrics drawn from
authoritative SOC operations and threat intelligence sources.

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

## Citation

```bibtex
@dataset{xpertsystems_cyb008_sample_2026,
  title  = {CYB008: Synthetic SOC Alert Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/cyb008-sample}
}
```

## Generation Details

- Generator version : 1.2.0
- Random seed       : 42
- Generated         : 2026-05-16 14:24:43 UTC
- Alert lifecycle   : Multi-phase state machine with SOAR / fatigue / storm
- Overall benchmark : 100.0 / 100  (grade A+)