README.md

---
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - cybersecurity
  - identity-security
  - account-takeover
  - mfa-bypass
  - ueba
  - zero-trust
  - apt
  - synthetic-data
  - lateral-movement
  - golden-ticket
pretty_name: CYB006 — Synthetic Login Activity Dataset (Sample)
size_categories:
  - 1K<n<10K
---

# CYB006 — Synthetic Login Activity Dataset (Sample)

**XpertSystems.ai Synthetic Data Platform · SKU: CYB006-SAMPLE · Version 1.0.0**

This is a **free preview** of the full **CYB006 — Synthetic Login Activity
Dataset** product. It contains roughly **~1.3% of the full dataset** at
identical schema, threat-actor-tier distribution, and statistical fingerprint,
so you can evaluate fit before licensing the full product.

> 🤖 **Trained baseline available:**
> [**xpertsystems/cyb006-baseline-classifier**](https://huggingface.co/xpertsystems/cyb006-baseline-classifier)
> — XGBoost + PyTorch MLP for **3-class user-risk-tier classification**
> (insider-threat scoring use case), stratified split, multi-seed evaluation
> (ROC-AUC 0.812 ± 0.048). **Includes a structural-leakage diagnostic on
> the threat-actor detection task** that buyers planning ATO / threat-actor
> ML work should read first.

| File                       | Rows (sample) | Rows (full)   | Description                                  |
|----------------------------|---------------|---------------|----------------------------------------------|
| `identity_topology.csv`    | ~150           | ~3,200        | Identity domain registry                     |
| `user_risk_summary.csv`    | ~200           | ~6,500        | Per-user risk aggregates                     |
| `login_sessions.csv`       | ~5,000         | ~377,000      | Per-session login records (primary file)     |
| `auth_events.csv`          | ~31,900        | ~750,000      | Discrete authentication event log            |

## Dataset Summary

CYB006 simulates enterprise login activity as a **6-phase session state
machine** across diverse identity infrastructures, with:

- **4 threat-actor capability tiers**: script_kiddie, opportunistic,
  advanced_persistent_threat (APT), nation_state — with per-tier credential
  attack patterns, MFA bypass propensity, lateral hop distributions, and
  Golden Ticket / Pass-the-Hash abuse rates
- **8 identity domain types**: on-premises AD, Azure AD, Okta, hybrid_joined,
  SAML federated, zero_trust_ztna, PAW (privileged access workstation),
  SaaS application portal — each with distinct detection_strength and
  resilience scores
- **MFA challenge methods**: disabled, SMS, TOTP, push notification,
  phishing-resistant FIDO2 — with per-method bypass propensity calibration
- **6 session lifecycle phases**: pre_auth_probe, credential_submission,
  mfa_challenge, session_active, lateral_traversal, session_termination
- **Geo-velocity modeling** with impossible travel detection via Haversine
  distance and per-user expected geolocation baselines
- **UEBA scoring** with calibrated false-positive rates
- **Conditional Access (CA) policy enforcement** modeling — ZTNA block
  strength tunable per architecture

## Trained Baseline Available

A working baseline classifier trained on this sample is published at
**[xpertsystems/cyb006-baseline-classifier](https://huggingface.co/xpertsystems/cyb006-baseline-classifier)**.

| Component | Detail |
|---|---|
| Primary task | **3-class user_risk_tier classification** (insider-threat scoring) |
| Diagnostic | Audit of threat-actor detection on this sample (see `leakage_diagnostic.json`) |
| Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
| Features | 34 per-user features (aggregates + non-leaky session aggregates + engineered) |
| Split | **Stratified by user_risk_tier** — user-level task, n=200 |
| Validation | Single seed + multi-seed aggregate across 10 seeds |
| Demo | `inference_example.ipynb` — end-to-end copy-paste |
| Headline metrics | XGBoost: accuracy 0.700 ± 0.082, macro ROC-AUC 0.812 ± 0.048 (multi-seed) |

**Important diagnostic finding for buyers planning threat-actor detection
work:** the model card documents that this sample's threat-actor-vs-legitimate
session populations have **non-overlapping anomaly score distributions**
across at least six feature groups (velocity, timestamp, credential attempt
count, login outcome, geo country, device trust). As a result, a plain
XGBoost achieves 100% test accuracy on threat-actor binary detection that
does not reflect real-world detection difficulty. The baseline model
targets `user_risk_tier` instead, which is a legitimate ML task on the
sample. See the model card's [Leakage diagnostic](https://huggingface.co/xpertsystems/cyb006-baseline-classifier#leakage-diagnostic)
section for the full audit and recommendations.

## Calibrated Benchmark Targets

The full product is calibrated to **12 benchmark validation tests** drawn
from authoritative identity security sources (Microsoft Digital Defense
Report, Okta Customer Identity Trends, Verizon DBIR, CISA Joint Advisories,
Mandiant M-Trends, MITRE ATT&CK Evaluations, Gartner IAM Hype Cycle,
KuppingerCole Leadership Compass).

**Benchmark categories** (calibrated in both sample and full product):

1. **Credential attack velocity** — brute force (~50 RPS), password spray (<1 RPS)
2. **Account takeover rate by tier** — graduated by attacker capability
3. **MFA bypass rate** — FIDO2 ≤1%, push/SMS variable
4. **Impossible travel rate** — 7-12% of sessions
5. **Lateral movement depth** — capped per tier (script_kiddie ≤1.2 → nation_state ≤14)
6. **Privilege escalation rate** — conditional on lateral movement
7. **MFA fatigue burst timing** — Poisson λ=7 burst pattern
8. **UEBA false positive rate** — calibrated to 10-14% range
9. **Golden Ticket / Pass-the-Hash detection gap** — stealth modeling
10. **Session duration anomaly separation** — KL divergence proxy
11. **Conditional Access block rate** — ZTNA ≥88% for untrusted
12. **Kill-chain completion rate** — phase-to-phase progression

Sample benchmark results:

| Test | Description | Verdict |
|------|-------------|---------|
| T01 | Credential Attack Velocity | ✓ PASS |
| T02 | Account Takeover Rate by Tier | ✓ PASS |
| T03 | MFA Bypass Rate (FIDO2) | ✓ PASS |
| T04 | Impossible Travel Rate | ✓ PASS |
| T05 | Lateral Movement Depth by Tier | ✓ PASS |
| T06 | Privilege Escalation Rate | ✓ PASS |
| T07 | MFA Fatigue Burst Detection | ✓ PASS |
| T08 | UEBA False Positive Rate | ✓ PASS |
| T09 | Golden Ticket / PtH Detection Gap | ✓ PASS |
| T10 | Session Duration Anomaly Separation | ✓ PASS |
| T11 | Conditional Access Block Rate (ZTNA) | ✓ PASS |
| T12 | Kill-Chain Completion Rate | ✓ PASS |

*Note: some benchmarks (e.g. nation-state account takeover rates, Golden
Ticket detection) require larger sample sizes to converge tightly because
they're conditional on small attacker-tier subsets (nation_state ≈ 2% of
all sessions, APT ≈ 3%). The full product demonstrates all 12 benchmarks
with strong statistical power.*

## Schema Highlights

### `login_sessions.csv` (primary file)

| Column                          | Type    | Description                                  |
|---------------------------------|---------|----------------------------------------------|
| session_id                      | string  | Unique session identifier                    |
| user_id                         | string  | User identifier (FK to user_risk_summary)    |
| session_timestamp_utc           | string  | ISO timestamp                                |
| session_phase                   | string  | 1 of 6 phases                                |
| login_outcome                   | string  | success / failed / mfa_required / blocked    |
| source_ip_hash                  | string  | SHA-256 pseudonymised source IP              |
| geo_country_code                | string  | ISO 3166 country code                        |
| geo_city_hash                   | string  | Hashed city locator                          |
| device_id_hash                  | string  | Hashed device fingerprint                    |
| device_trust_level              | string  | unknown / known / managed / compliant        |
| authentication_method           | string  | password / sso / certificate / api_key       |
| mfa_challenge_type              | string  | disabled / sms / totp / push / fido2         |
| mfa_response_latency_ms         | int     | MFA response latency                         |
| credential_attempt_count        | int     | Attempts before success                      |
| session_duration_seconds        | int     | Session length                               |
| target_application_id           | string  | Application accessed                         |
| privilege_level_accessed        | string  | standard / power_user / admin / domain_admin |
| user_risk_tier                  | string  | low / medium / high / critical               |
| threat_actor_capability_tier    | string  | script_kiddie / opportunistic / apt / nation_state (target) |
| geo_anomaly_score               | float   | Geographic anomaly score (0–1)               |
| velocity_anomaly_score          | float   | Login velocity anomaly score (0–1)           |
| impossible_travel_flag          | int     | Boolean — impossible travel detected         |

### `user_risk_summary.csv` (per-user aggregates)

| Column                          | Type    | Description                                  |
|---------------------------------|---------|----------------------------------------------|
| user_id                         | string  | User identifier                              |
| user_risk_tier                  | string  | Risk tier classification target              |
| total_login_attempts            | int     | Total login attempts in window               |
| successful_logins               | int     | Successful logins                            |
| failed_logins                   | int     | Failed logins                                |
| mfa_failures                    | int     | MFA challenge failures                       |
| impossible_travel_events        | int     | Count of impossible travel detections        |
| lateral_hop_count               | int     | Total lateral movement hops                  |
| privilege_escalations           | int     | Privilege escalation count                   |
| account_lockout_count           | int     | Account lockout events                       |
| geo_dispersion_score            | float   | Geographic dispersion (0–1)                  |
| login_velocity_score            | float   | Velocity anomaly (0–1)                       |
| session_anomaly_rate            | float   | Fraction of anomalous sessions               |
| ueba_alert_count                | int     | UEBA alerts raised                           |
| threat_actor_flag               | int     | Boolean — threat actor                       |
| account_takeover_flag           | int     | Boolean — account takeover detected          |
| overall_identity_risk_score     | float   | Composite identity risk (0–1)                |
| insider_threat_indicator_score  | float   | Insider threat composite (0–1)               |

See `auth_events.csv` and `identity_topology.csv` for the event log and
identity domain schemas respectively.

## Suggested Use Cases

- Training **insider threat scoring** models —
  [worked example available](https://huggingface.co/xpertsystems/cyb006-baseline-classifier)
- **Account takeover (ATO) detection** model development (see leakage diagnostic in the baseline model card before training)
- **Threat-actor tier classification** — 4-class with realistic class imbalance (see leakage diagnostic before training)
- **Impossible travel detection** — geo-velocity feature engineering
- **MFA bypass detection** — distinguish FIDO2 anomalies from push fatigue
- **Lateral movement detection** — session-graph traversal patterns
- **Golden Ticket / Pass-the-Hash** detection benchmarking
- **UEBA precision/recall tuning** with calibrated false-positive baselines
- **Conditional Access policy effectiveness** simulation
- **Zero Trust posture validation** — ZTNA block rate analysis

## Loading the Data

```python
import pandas as pd

sessions = pd.read_csv("login_sessions.csv")
users    = pd.read_csv("user_risk_summary.csv")
events   = pd.read_csv("auth_events.csv")
domains  = pd.read_csv("identity_topology.csv")

# Join session data with user-level risk labels
enriched = sessions.merge(users, on="user_id", how="left",
                          suffixes=("", "_user"))

# Threat-actor tier classification target (4-class) — see leakage diagnostic
y_tier = sessions["threat_actor_capability_tier"]

# Binary account-takeover detection target
y_ato = users["account_takeover_flag"]

# Binary impossible-travel target
y_it = sessions["impossible_travel_flag"]
```

For a worked end-to-end example with user-risk-tier classification,
stratified splitting, and feature engineering, see the inference notebook
in the [baseline classifier repo](https://huggingface.co/xpertsystems/cyb006-baseline-classifier/blob/main/inference_example.ipynb).

## 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 CYB006 dataset includes **~1.1 million rows** across all four files,
with 12 calibrated benchmark validation tests drawn from authoritative
identity security and threat intelligence sources.

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

## Citation

```bibtex
@dataset{xpertsystems_cyb006_sample_2026,
  title  = {CYB006: Synthetic Login Activity Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/cyb006-sample}
}
```

## Generation Details

- Generator version : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-16 14:13:20 UTC
- Session model     : 6-phase login lifecycle state machine
- Benchmark tests   : 12/12 passing