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 — 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.
| 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:
Three structural oracles in the data (
alert_lifecycle_phase,automation_resolved,escalation_flag) deterministically encode theresolution_outcomelabel. 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.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.
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
- 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
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.
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
@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+)