README.md

---
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)