xpertsystems/cyb006-sample

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 — 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.

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 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
• 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

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.

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