xpertsystems/cyb010-sample

CYB010 — Synthetic Security Event Log Dataset (Sample)

XpertSystems.ai Synthetic Data Platform · SKU: CYB010-SAMPLE · Version 1.0.0

This is a free preview of the full CYB010 — Synthetic Security Event Log Dataset product. It contains roughly ~10% of the full dataset at identical schema, MITRE ATT&CK technique coverage, and statistical fingerprint, so you can evaluate fit before licensing the full product.

File	Rows (sample)	Rows (full)	Description
host_inventory.csv	~400	~3,200	Enterprise host inventory
incident_summary.csv	~500	~4,800	Per-incident campaign summaries
alert_records.csv	~5,162	~42,000	SIEM alert records with triage labels
security_events.csv	~21,896	~500,000	Raw security event log records (primary)

Dataset Summary

CYB010 simulates enterprise security event logs as a 5-phase attack lifecycle state machine across realistic detection environments, with:

• 5 threat actor profiles: benign_user, script_kiddie, insider_threat, advanced_persistent_threat (APT), nation_state_actor — each with distinct fileless execution ratios, log tampering propensities, off-hours bias, and dwell time distributions
• 4 defender posture tiers: minimal, standard, hardened, zero_trust — graduated detection_strength (0.42 → 0.93) and false-positive rates
• 5-phase attack lifecycle: dormant → initial_access → lateral_movement → persistence_establishment → exfiltration_or_impact
• 8 SIEM platform log formats with realistic per-vendor parsing: Splunk KV, Microsoft Sentinel JSON, IBM QRadar LEEF, Elastic ECS, Google Chronicle UDM, AWS Security Hub, Palo Alto XSIAM, ArcSight CEF
• MITRE ATT&CK v14 coverage — 50 techniques across 14 tactics, mapped to all malicious events via T-codes
• Time-of-day + day-of-week noise model — Poisson background traffic with off-hours and weekend multipliers
• C2 beacon periodicity modeling — configurable mean interval and jitter for command-and-control detection
• IOC seeding density — calibrated indicator-of-compromise injection for threat intel detection benchmarking

Calibrated Benchmark Targets

The full product is calibrated to 6 benchmark validation tests drawn from authoritative SOC operations and threat intelligence research (SANS SOC Survey, IBM Cost of Data Breach, Mandiant M-Trends, Verizon DBIR, CISA Joint Advisories, MITRE ATT&CK Evaluations, Splunk State of Security).

Sample benchmark results:

Test	Target	Observed	Verdict
false_positive_alert_rate	0.4500	0.5271	~ MARGINAL
mean_dwell_time_hours	504.0	479.4	✓ PASS
lateral_movement_hop_rate	0.0950	0.1040	✓ PASS
alert_suppression_rate	0.3800	0.4291	✓ PASS
exfiltration_attempted_rate	0.3100	0.3380	✓ PASS
patch_compliance_ratio	0.7200	0.7312	✓ PASS

Schema Highlights

security_events.csv (primary file, raw event logs)

Column	Type	Description
event_id	string	Unique event identifier
incident_id	string	Parent incident FK (nullable for benign)
host_id	string	FK to host_inventory.csv
timestamp_utc	string	ISO timestamp
event_type	string	process_create / network_connect / file_write / login / etc.
log_format	string	splunk_kv / sentinel_json / qradar_leef / elastic_ecs / etc.
raw_log	string	Vendor-formatted log line (key=value, JSON, LEEF, etc.)
source_ip	string	Source IP address
dest_ip	string	Destination IP address
user	string	User account associated with event
process_name	string	Process executable name
command_line	string	Command line (truncated)
mitre_technique_id	string	T-number (e.g. T1059.001) — empty for benign
mitre_tactic	string	ATT&CK tactic category
threat_actor_profile	string	benign_user / script_kiddie / insider / apt / nation_state
attack_phase	string	1 of 5 lifecycle phases
is_off_hours	int	Boolean — outside 9-17 local

alert_records.csv (SIEM alerts)

Column	Type	Description
alert_id	string	Unique alert identifier
triggering_event_id	string	FK to triggering security event
host_id	string	FK to host inventory
alert_severity	string	info / low / medium / high / critical
detection_rule	string	Rule name that fired
label_false_positive	int	Boolean — ground-truth FP label
suppressed_flag	int	Boolean — alert suppressed
ioc_matched	int	Boolean — IOC database match
triage_outcome	string	true_positive / false_positive / suppressed / escalated

incident_summary.csv (per-incident)

Column	Type	Description
incident_id	string	Unique incident identifier
threat_actor_profile	string	4-class actor target
defender_posture	string	4-tier defender maturity
dwell_time_hours	float	End-to-end attacker dwell
lateral_movement_hops	int	Count of lateral movement events
exfiltration_attempted_flag	int	Boolean — exfil attempted
campaign_success_flag	int	Boolean — campaign succeeded
total_events	int	Events generated by this incident
total_alerts	int	Alerts triggered

host_inventory.csv (enterprise hosts)

Column	Type	Description
host_id	string	Unique host identifier
hostname	string	Hostname
os_platform	string	windows / linux / macos / etc.
defender_posture	string	minimal / standard / hardened / zero_trust
patch_compliance_level	float	Patch compliance score (0–1)
ip_address	string	Primary IP

Suggested Use Cases

• Training SIEM alert triage models — predict true_positive vs false_positive
• MITRE ATT&CK technique classification from raw log lines
• Threat actor attribution — 5-class with realistic class imbalance
• Multi-format log parser training — 8 SIEM vendor formats in one corpus
• Dwell time forecasting under varying defender posture
• Lateral movement detection from event sequences
• C2 beacon detection — periodic vs aperiodic network connections
• IOC matching effectiveness — calibrated 18.5% match rate baseline
• Log tampering detection — APT log-tamper-prob 0.35 baseline
• Off-hours anomaly detection — APT off-hours bias 0.64

Loading the Data

import pandas as pd

events    = pd.read_csv("security_events.csv")
alerts    = pd.read_csv("alert_records.csv")
incidents = pd.read_csv("incident_summary.csv")
hosts     = pd.read_csv("host_inventory.csv")

# Join events to host context
enriched = events.merge(hosts, on="host_id", how="left",
                        suffixes=("", "_host"))

# Join alerts back to source event and incident
alerts_full = alerts.merge(events, left_on="triggering_event_id",
                           right_on="event_id", how="left",
                           suffixes=("_alert", "_event"))

# Multi-class threat actor profile target
y_actor = events["threat_actor_profile"]

# Binary false-positive prediction target
y_fp = alerts["label_false_positive"]

# Multi-class MITRE technique target (filter to malicious events)
malicious = events[events["mitre_technique_id"] != ""]
y_technique = malicious["mitre_technique_id"]

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 CYB010 dataset includes ~550,000 rows across all four files, with calibrated benchmark validation against 6 metrics drawn from authoritative SOC operations and threat intelligence sources.

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

Citation

@dataset{xpertsystems_cyb010_sample_2026,
  title  = {CYB010: Synthetic Security Event Log Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/cyb010-sample}
}

Generation Details

• Generator version : 1.0.0
• Random seed : 42
• Generated : 2026-05-16 14:37:46 UTC
• Attack lifecycle : 5-phase finite state machine
• Overall benchmark : 95.3 / 100 (grade A)