| ---
|
| 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**](https://huggingface.co/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](https://huggingface.co/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`](https://huggingface.co/xpertsystems/cyb010-baseline-classifier/blob/main/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](https://huggingface.co/xpertsystems/cyb010-baseline-classifier)
|
| - 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
|
|
|
| ```python
|
| 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](https://huggingface.co/xpertsystems/cyb010-baseline-classifier).
|
|
|
| ## 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
|
|
|
| ```bibtex
|
| @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)
|
| |
