--- 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 🤖 **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+)