xpertsystems/cyb008-sample

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.

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

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 true_positive vs false_positive
• MITRE ATT&CK tactic classification from alert features
• 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 for at-risk alerts
• 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"))

# Binary true-positive classification target
y_tp = alerts["resolution_outcome"].isin([
    "true_positive_remediated",
    "true_positive_escalated",
    "incident_declared",
]).astype(int)

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

# Binary SLA breach prediction target
y_sla = alerts["sla_breached_flag"]

# Per-incident severity classification
y_severity = incidents["incident_severity"]

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+)