README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - cybersecurity
  - siem
  - security-logs
  - mitre-attack
  - apt
  - synthetic-data
  - alert-triage
  - soc-operations
  - threat-detection
  - splunk
pretty_name: CYB010 — Synthetic Security Event Log Dataset (Sample)
size_categories:
  - 10K<n<100K

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.

🤖 Trained baseline + leakage diagnostic available: xpertsystems/cyb010-baseline-classifier — XGBoost + PyTorch MLP for 5-class attack lifecycle phase classification (the dataset's headline target), group-aware split by incident_id, multi-seed evaluation (acc 0.936 ± 0.007, ROC-AUC 0.988 ± 0.001 — tightest AUC std in the catalog). Includes a comprehensive leakage_diagnostic.json documenting 11 oracle paths discovered across the dataset's targets and 2 README-suggested headline targets that are unlearnable on the sample after honest leak removal. Buyers planning SIEM ML work should read the diagnostic first.

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

Trained Baseline + Leakage Audit Available

A working baseline classifier + comprehensive leakage diagnostic is published at xpertsystems/cyb010-baseline-classifier.

Component	Detail
Primary task	5-class attack_lifecycle_phase classification (the dataset's headline target)
Secondary artifact	leakage_diagnostic.json — 11 oracle paths + 2 unlearnable targets
Models	XGBoost (model_xgb.json) + PyTorch MLP (model_mlp.safetensors)
Features	87 (after one-hot encoding); pipeline included as feature_engineering.py
Split	Group-aware (GroupShuffleSplit on incident_id) — 500 incidents, ~75 in test fold
Validation	Single seed + multi-seed aggregate across 10 seeds
Demo	inference_example.ipynb — end-to-end copy-paste
Headline metrics	XGBoost: accuracy 0.936 ± 0.007, macro ROC-AUC 0.988 ± 0.001 (multi-seed)

Important findings for buyers planning CYB010 ML work (full detail in leakage_diagnostic.json):

11 oracle paths documented across two task families:

Phase target oracles (6 paths) — drop these when training your own phase classifier:

1. mitre_tactic == "benign" → 100% benign_background phase
2. mitre_technique_id → mitre_tactic (perfect ATT&CK-by-design oracle)
3. label_malicious == False → 100% benign_background
4. threat_actor_id == "NONE" → 100% benign
5. threat_actor_profile == "benign_user" → 100% benign
6. event_type (many values phase-specific; e.g. c2_beacon_outbound → 100% exfil)

Alert TP target oracles (7 paths) — label_true_positive on alert_records.csv is 100% accurate with any single one of these intact:

1. alert_category == "false_positive_noise" → 100% FP
2. label_false_positive (mirror target)
3. time_to_detect_seconds == 0 → 100% FP (sentinel)
4. correlated_chain_length == 1 → near-100% FP (sentinel)
5. analyst_triage_priority ∈ {P1,P2,P3} → 100% TP
6. suppression_reason == NaN → 100% TP
7. alert_rule_name (rule names encode answer)

2 README-suggested headline targets unlearnable after honest leak removal:

• threat_actor_profile 4-class malicious-only (acc 0.55 vs majority 0.61)
• event_class 12-class (acc 0.35 vs majority 0.42)

Viable secondary task: label_true_positive binary on alerts — acc 0.80, AUC 0.89 after dropping all 7 oracle columns. Documented in the diagnostic.

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 attack lifecycle phase classification models (the baseline ships this) — worked example available
• Training SIEM alert triage models — predict true_positive vs false_positive (see leakage diagnostic — 7 oracle columns must be dropped; honest acc 0.80 / AUC 0.89)
• MITRE ATT&CK technique classification from raw log lines
• Threat actor attribution — 5-class with realistic class imbalance (see leakage diagnostic — 4-class malicious-only is unlearnable; 5-class works only because benign separation is trivial)
• 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="correlated_event_ids",
                           right_on="event_id", how="left",
                           suffixes=("_alert", "_event"))

# 5-class attack lifecycle phase target (the baseline ships this)
y_phase = events["attack_lifecycle_phase"]

# Multi-class threat actor profile target (5-class with benign;
# see leakage diagnostic — 4-class malicious-only is unlearnable)
y_actor = events["threat_actor_profile"]

# Binary false-positive prediction target
# (see leakage diagnostic — 7 oracle columns must be dropped)
y_fp = alerts["label_false_positive"]

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

For a worked end-to-end example with attack_lifecycle_phase 5-class classification, group-aware splitting, feature engineering, and the full 11-oracle-path leakage audit, see 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 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)