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.
| 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
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):
- Credential attack velocity — brute force (~50 RPS), password spray (<1 RPS)
- Account takeover rate by tier — graduated by attacker capability
- MFA bypass rate — FIDO2 ≤1%, push/SMS variable
- Impossible travel rate — 7-12% of sessions
- Lateral movement depth — capped per tier (script_kiddie ≤1.2 → nation_state ≤14)
- Privilege escalation rate — conditional on lateral movement
- MFA fatigue burst timing — Poisson λ=7 burst pattern
- UEBA false positive rate — calibrated to 10-14% range
- Golden Ticket / Pass-the-Hash detection gap — stealth modeling
- Session duration anomaly separation — KL divergence proxy
- Conditional Access block rate — ZTNA ≥88% for untrusted
- 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 account takeover (ATO) detection models
- Threat-actor tier classification — 4-class with realistic class imbalance
- 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
- Insider threat scoring with composite behavioral indicators
- Zero Trust posture validation — ZTNA block rate analysis
Loading the Data
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)
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"]
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
@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