| ---
|
| license: cc-by-nc-4.0
|
| task_categories:
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| - tabular-classification
|
| - time-series-forecasting
|
| tags:
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| - cybersecurity
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| - identity-security
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| - account-takeover
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| - mfa-bypass
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| - ueba
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| - zero-trust
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| - apt
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| - synthetic-data
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| - lateral-movement
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| - golden-ticket
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| pretty_name: CYB006 — Synthetic Login Activity Dataset (Sample)
|
| size_categories:
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| - 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
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| Dataset** product. It contains roughly **~1.3% of the full dataset** at
|
| identical schema, threat-actor-tier distribution, and statistical fingerprint,
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| so you can evaluate fit before licensing the full product.
|
|
|
| > 🤖 **Trained baseline available:**
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| > [**xpertsystems/cyb006-baseline-classifier**](https://huggingface.co/xpertsystems/cyb006-baseline-classifier)
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| > — XGBoost + PyTorch MLP for **3-class user-risk-tier classification**
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| > (insider-threat scoring use case), stratified split, multi-seed evaluation
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| > (ROC-AUC 0.812 ± 0.048). **Includes a structural-leakage diagnostic on
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| > the threat-actor detection task** that buyers planning ATO / threat-actor
|
| > ML work should read first.
|
|
|
| | File | Rows (sample) | Rows (full) | Description |
|
| |----------------------------|---------------|---------------|----------------------------------------------|
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| | `identity_topology.csv` | ~150 | ~3,200 | Identity domain registry |
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| | `user_risk_summary.csv` | ~200 | ~6,500 | Per-user risk aggregates |
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| | `login_sessions.csv` | ~5,000 | ~377,000 | Per-session login records (primary file) |
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| | `auth_events.csv` | ~31,900 | ~750,000 | Discrete authentication event log |
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|
|
| ## Dataset Summary
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|
|
| CYB006 simulates enterprise login activity as a **6-phase session state
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| machine** across diverse identity infrastructures, with:
|
|
|
| - **4 threat-actor capability tiers**: script_kiddie, opportunistic,
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| advanced_persistent_threat (APT), nation_state — with per-tier credential
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| attack patterns, MFA bypass propensity, lateral hop distributions, and
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| Golden Ticket / Pass-the-Hash abuse rates
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| - **8 identity domain types**: on-premises AD, Azure AD, Okta, hybrid_joined,
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| SAML federated, zero_trust_ztna, PAW (privileged access workstation),
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| SaaS application portal — each with distinct detection_strength and
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| resilience scores
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| - **MFA challenge methods**: disabled, SMS, TOTP, push notification,
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| phishing-resistant FIDO2 — with per-method bypass propensity calibration
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| - **6 session lifecycle phases**: pre_auth_probe, credential_submission,
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| mfa_challenge, session_active, lateral_traversal, session_termination
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| - **Geo-velocity modeling** with impossible travel detection via Haversine
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| distance and per-user expected geolocation baselines
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| - **UEBA scoring** with calibrated false-positive rates
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| - **Conditional Access (CA) policy enforcement** modeling — ZTNA block
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| strength tunable per architecture
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|
|
| ## Trained Baseline Available
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|
|
| A working baseline classifier trained on this sample is published at
|
| **[xpertsystems/cyb006-baseline-classifier](https://huggingface.co/xpertsystems/cyb006-baseline-classifier)**.
|
|
|
| | Component | Detail |
|
| |---|---|
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| | Primary task | **3-class user_risk_tier classification** (insider-threat scoring) |
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| | Diagnostic | Audit of threat-actor detection on this sample (see `leakage_diagnostic.json`) |
|
| | Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
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| | Features | 34 per-user features (aggregates + non-leaky session aggregates + engineered) |
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| | Split | **Stratified by user_risk_tier** — user-level task, n=200 |
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| | Validation | Single seed + multi-seed aggregate across 10 seeds |
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| | Demo | `inference_example.ipynb` — end-to-end copy-paste |
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| | 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
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| session populations have **non-overlapping anomaly score distributions**
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| across at least six feature groups (velocity, timestamp, credential attempt
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| count, login outcome, geo country, device trust). As a result, a plain
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| XGBoost achieves 100% test accuracy on threat-actor binary detection that
|
| does not reflect real-world detection difficulty. The baseline model
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| targets `user_risk_tier` instead, which is a legitimate ML task on the
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| sample. See the model card's [Leakage diagnostic](https://huggingface.co/xpertsystems/cyb006-baseline-classifier#leakage-diagnostic)
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| section for the full audit and recommendations.
|
|
|
| ## Calibrated Benchmark Targets
|
|
|
| The full product is calibrated to **12 benchmark validation tests** drawn
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| from authoritative identity security sources (Microsoft Digital Defense
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| Report, Okta Customer Identity Trends, Verizon DBIR, CISA Joint Advisories,
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| Mandiant M-Trends, MITRE ATT&CK Evaluations, Gartner IAM Hype Cycle,
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| KuppingerCole Leadership Compass).
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|
|
| **Benchmark categories** (calibrated in both sample and full product):
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|
|
| 1. **Credential attack velocity** — brute force (~50 RPS), password spray (<1 RPS)
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| 2. **Account takeover rate by tier** — graduated by attacker capability
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| 3. **MFA bypass rate** — FIDO2 ≤1%, push/SMS variable
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| 4. **Impossible travel rate** — 7-12% of sessions
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| 5. **Lateral movement depth** — capped per tier (script_kiddie ≤1.2 → nation_state ≤14)
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| 6. **Privilege escalation rate** — conditional on lateral movement
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| 7. **MFA fatigue burst timing** — Poisson λ=7 burst pattern
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| 8. **UEBA false positive rate** — calibrated to 10-14% range
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| 9. **Golden Ticket / Pass-the-Hash detection gap** — stealth modeling
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| 10. **Session duration anomaly separation** — KL divergence proxy
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| 11. **Conditional Access block rate** — ZTNA ≥88% for untrusted
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| 12. **Kill-chain completion rate** — phase-to-phase progression
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|
|
| Sample benchmark results:
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|
|
| | Test | Description | Verdict |
|
| |------|-------------|---------|
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| | T01 | Credential Attack Velocity | ✓ PASS |
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| | T02 | Account Takeover Rate by Tier | ✓ PASS |
|
| | T03 | MFA Bypass Rate (FIDO2) | ✓ PASS |
|
| | T04 | Impossible Travel Rate | ✓ PASS |
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| | T05 | Lateral Movement Depth by Tier | ✓ PASS |
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| | T06 | Privilege Escalation Rate | ✓ PASS |
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| | T07 | MFA Fatigue Burst Detection | ✓ PASS |
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| | T08 | UEBA False Positive Rate | ✓ PASS |
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| | T09 | Golden Ticket / PtH Detection Gap | ✓ PASS |
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| | T10 | Session Duration Anomaly Separation | ✓ PASS |
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| | T11 | Conditional Access Block Rate (ZTNA) | ✓ PASS |
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| | T12 | Kill-Chain Completion Rate | ✓ PASS |
|
|
|
| *Note: some benchmarks (e.g. nation-state account takeover rates, Golden
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| Ticket detection) require larger sample sizes to converge tightly because
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| 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 |
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| | user_id | string | User identifier (FK to user_risk_summary) |
|
| | session_timestamp_utc | string | ISO timestamp |
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| | session_phase | string | 1 of 6 phases |
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| | login_outcome | string | success / failed / mfa_required / blocked |
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| | source_ip_hash | string | SHA-256 pseudonymised source IP |
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| | geo_country_code | string | ISO 3166 country code |
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| | geo_city_hash | string | Hashed city locator |
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| | device_id_hash | string | Hashed device fingerprint |
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| | device_trust_level | string | unknown / known / managed / compliant |
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| | authentication_method | string | password / sso / certificate / api_key |
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| | mfa_challenge_type | string | disabled / sms / totp / push / fido2 |
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| | mfa_response_latency_ms | int | MFA response latency |
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| | credential_attempt_count | int | Attempts before success |
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| | session_duration_seconds | int | Session length |
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| | target_application_id | string | Application accessed |
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| | privilege_level_accessed | string | standard / power_user / admin / domain_admin |
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| | user_risk_tier | string | low / medium / high / critical |
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| | threat_actor_capability_tier | string | script_kiddie / opportunistic / apt / nation_state (target) |
|
| | geo_anomaly_score | float | Geographic anomaly score (0–1) |
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| | velocity_anomaly_score | float | Login velocity anomaly score (0–1) |
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| | 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 |
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| | successful_logins | int | Successful logins |
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| | failed_logins | int | Failed logins |
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| | mfa_failures | int | MFA challenge failures |
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| | impossible_travel_events | int | Count of impossible travel detections |
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| | lateral_hop_count | int | Total lateral movement hops |
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| | privilege_escalations | int | Privilege escalation count |
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| | account_lockout_count | int | Account lockout events |
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| | geo_dispersion_score | float | Geographic dispersion (0–1) |
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| | login_velocity_score | float | Velocity anomaly (0–1) |
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| | session_anomaly_rate | float | Fraction of anomalous sessions |
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| | ueba_alert_count | int | UEBA alerts raised |
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| | threat_actor_flag | int | Boolean — threat actor |
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| | account_takeover_flag | int | Boolean — account takeover detected |
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| | overall_identity_risk_score | float | Composite identity risk (0–1) |
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| | 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")
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| domains = pd.read_csv("identity_topology.csv")
|
|
|
| # Join session data with user-level risk labels
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| enriched = sessions.merge(users, on="user_id", how="left",
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| suffixes=("", "_user"))
|
|
|
| # Threat-actor tier classification target (4-class) — see leakage diagnostic
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| y_tier = sessions["threat_actor_capability_tier"]
|
|
|
| # Binary account-takeover detection target
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| y_ato = users["account_takeover_flag"]
|
|
|
| # Binary impossible-travel target
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| 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
|
|
|
