--- 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 🤖 **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)