| ---
|
| license: cc-by-nc-4.0
|
| task_categories:
|
| - tabular-classification
|
| - time-series-forecasting
|
| tags:
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| - cybersecurity
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| - soc-operations
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| - alert-triage
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| - mitre-attack
|
| - soar
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| - siem
|
| - synthetic-data
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| - incident-response
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| - analyst-fatigue
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| - false-positive-reduction
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| pretty_name: CYB008 — Synthetic SOC Alert Dataset (Sample)
|
| size_categories:
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| - 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
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| 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:**
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| > [**xpertsystems/cyb008-baseline-classifier**](https://huggingface.co/xpertsystems/cyb008-baseline-classifier)
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| > — XGBoost + PyTorch MLP for **5-class SOC alert triage outcome
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| > classification** (the README's first headline use case), stratified
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| > split, multi-seed evaluation (ROC-AUC 0.955 ± 0.003). **Includes a
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| > structural-leakage diagnostic** documenting three oracle columns
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| > dropped from the feature set, and a separate unlearnable-target
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| > 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 |
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| | `incident_summary.csv` | ~589 | ~4,800 | Per-incident aggregate outcomes |
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| | `alert_events.csv` | ~55,298 | ~48,000 | Discrete alert event log |
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| | `soc_alerts.csv` | ~9,200 | ~280,000 | Per-alert records (primary file) |
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|
|
| ## Dataset Summary
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|
|
| CYB008 simulates end-to-end Security Operations Centre (SOC) alert
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| lifecycles across enterprise detection environments, with:
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|
|
| - **Full MITRE ATT&CK tactic coverage** — alerts mapped to all 14
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| Enterprise tactics from reconnaissance through impact
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| - **Alert severity distribution** — info / low / medium / high / critical
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| / false_positive, with calibrated 45% false-positive baseline
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| - **SOC analyst tier modeling** — tier_1 / tier_2 / tier_3 / SOC manager
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| with differentiated MTTR by experience level
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| - **SOAR automation** — playbook trigger probability, auto-resolution
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| rate, automation coverage by alert type
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| - **Alert storm events** — Poisson-distributed alert bursts (2.5×–6×
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| amplification) simulating coordinated attacks or system failures
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| - **Analyst fatigue modeling** — utilization-driven burnout with MTTR
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| degradation past fatigue threshold (0.82)
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| - **Kill-chain correlated incidents** — alerts grouped into multi-stage
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| incidents when ≥3 ATT&CK tactics observed
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| - **SLA tracking** — severity-dependent SLA thresholds (critical 60min,
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| high 240min, medium 480min, low 1440min)
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| - **Detection source mix** — EDR, SIEM, NDR, IDS, UEBA, CASB, deception,
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| threat intel feeds
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| - **Rule drift modeling** — periodic rule-noise amplification simulating
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| detection-engineering signal decay
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|
|
| ## 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) |
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| | Diagnostic | Structural-leakage audit (3 oracle columns dropped) + unlearnable-target finding for `mitre_tactic` |
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| | Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
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| | 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) |
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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 0.777 ± 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`,
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| `automation_resolved`, `escalation_flag`) deterministically encode
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| the `resolution_outcome` label. With these columns present, a
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| plain XGBoost achieves 100% accuracy. The baseline excludes them
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| to demonstrate honest learning — and the documented honest result
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| (acc 0.78, AUC 0.96) is genuinely useful.
|
|
|
| 2. **MITRE ATT&CK tactic classification is NOT learnable on this
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| sample.** The README lists tactic classification as a top use case,
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| but feature distributions are nearly identical across all 12
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| tactics. A trained model performs *below* majority baseline
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| (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
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| majority 0.82). Documented as out-of-scope.
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|
|
| 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
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|
|
| The full product is calibrated to 12 benchmark validation tests drawn from
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| authoritative SOC operations research (SANS SOC Survey, IBM Cost of Data
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| Breach, Mandiant M-Trends, Forrester Wave SOAR, Gartner SIEM Magic
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| Quadrant, SOC.OS, CrowdStrike, Splunk State of Security, Verizon DBIR).
|
|
|
| Sample benchmark results:
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|
|
| | Test | Target | Observed | Verdict |
|
| |------|--------|----------|---------|
|
| | false_positive_rate | 0.4500 | 0.4518 | ✓ PASS |
|
| | mttd_minutes_mean | 132.0 | 137.1 | ✓ PASS |
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| | mttr_minutes_mean | 480.0 | 494.9 | ✓ PASS |
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| | escalation_rate | 0.2200 | 0.2038 | ✓ PASS |
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| | auto_resolution_rate | 0.3100 | 0.2872 | ✓ PASS |
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| | alert_volume_rate | 0.1650 | 0.1840 | ✓ PASS |
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| | analyst_fatigue_score | 0.6400 | 0.6457 | ✓ PASS |
|
| | soar_playbook_execution_rate | 0.4200 | 0.4223 | ✓ PASS |
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| | incident_declaration_rate | 0.0850 | 0.0640 | ✓ PASS |
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| | true_positive_rate | 0.3800 | 0.3442 | ✓ PASS |
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| | kill_chain_completion_rate | 0.1450 | 0.1290 | ✓ PASS |
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| | sla_breach_rate | 0.1800 | 0.1775 | ✓ PASS |
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|
|
| *Note: every CYB008 benchmark is directly parametrised by the generator
|
| (e.g. `soar_trigger_prob=0.42` produces `soar_playbook_execution_rate=0.42`).
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| Calibration is precise even at sample scale. The full product produces the
|
| same calibration across 28× more data.*
|
|
|
| ## Schema Highlights
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|
|
| ### `soc_alerts.csv` (primary file)
|
|
|
| | Column | Type | Description |
|
| |---------------------------------|---------|----------------------------------------------|
|
| | alert_id | string | Unique alert identifier |
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| | incident_id | string | Parent incident FK (nullable) |
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| | soc_id | string | SOC environment FK |
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| | analyst_id | string | Assigned analyst FK |
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| | alert_timestamp | string | ISO timestamp |
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| | alert_severity | string | info / low / medium / high / critical / false_positive |
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| | mitre_tactic | string | 1 of 14 ATT&CK tactics |
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| | mitre_technique_id | string | T-number (e.g. T1059.001) |
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| | detection_source | string | edr / siem / ndr / ids / ueba / casb / etc. |
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| | triage_score | float | Initial triage priority (0–1) |
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| | enrichment_score | float | Threat-intel enrichment quality (0–1) |
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| | escalation_flag | int | Boolean — escalated to tier 2/3 |
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| | automation_resolved | int | Boolean — SOAR auto-resolved |
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| | soar_playbook_triggered | int | Boolean — SOAR playbook executed |
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| | mttd_minutes | float | Mean time to detect |
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| | mttr_minutes | float | Mean time to respond |
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| | sla_breached_flag | int | Boolean — SLA breached |
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| | resolution_outcome | string | true_positive / false_positive / duplicate / suppressed |
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| | analyst_tier | string | tier_1 / tier_2 / tier_3 / manager |
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| | storm_event_flag | int | Boolean — part of alert storm |
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| | kill_chain_stage | int | Position in kill chain (0 if standalone) |
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|
|
| ### `incident_summary.csv` (per-incident outcome)
|
|
|
| | Column | Type | Description |
|
| |---------------------------------|---------|----------------------------------------------|
|
| | incident_id | string | Identifier |
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| | soc_id, analyst_id | string | Identifiers |
|
| | n_alerts_correlated | int | Alerts grouped into this incident |
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| | kill_chain_stages_observed | int | Distinct ATT&CK tactics in chain |
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| | incident_severity | string | Composite severity |
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| | incident_resolution_outcome | string | true_positive / false_positive / partial |
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| | analyst_fatigue_score | float | Avg fatigue during incident (0–1) |
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| | incident_duration_minutes | float | End-to-end response time |
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|
|
| See `alert_events.csv` and `soc_topology.csv` for the discrete event log
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| 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
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| 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
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| import pandas as pd
|
|
|
| alerts = pd.read_csv("soc_alerts.csv")
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| incidents = pd.read_csv("incident_summary.csv")
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| events = pd.read_csv("alert_events.csv")
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| topology = pd.read_csv("soc_topology.csv")
|
|
|
| # Join alerts with analyst context
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| enriched = alerts.merge(topology, on=["soc_id", "analyst_id"], how="left",
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| suffixes=("", "_analyst"))
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|
|
| # 5-class triage outcome target (the README's first headline use case)
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| y_outcome = alerts["resolution_outcome"]
|
|
|
| # Binary true-positive collapse (for binary triage)
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| y_tp = alerts["resolution_outcome"].isin([
|
| "true_positive_remediated", "true_positive_escalated",
|
| ]).astype(int)
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|
|
| # Multi-class ATT&CK tactic classification target — see leakage diagnostic
|
| y_tactic = alerts["mitre_tactic"]
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|
|
| # Binary SLA breach prediction target — see leakage diagnostic
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| y_sla = alerts["sla_breached_flag"]
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| ```
|
|
|
| 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)},
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| author = {XpertSystems.ai},
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| 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+)
|
| |
