SCHEMA.md

Aestrea Dark Pool Financial Anomaly Pack — Schema

One row = one financial activity sequence (warm-up → burst). All records share the same seven top-level fields.

Schema version: 1.0.0-dark-pool-sample

Top-level fields

schema_version — string

Schema identifier. Constant within a sample release.

event — struct

Identifier fields and the overall label/severity for the sequence.

Field	Type	Notes
id	string	Stable event identifier, e.g., DARKPOOL-100000.
trace_id	string (UUID)	Cross-links telemetry within the sequence.
timestamp	string (ISO-8601)	Sequence anchor time (burst start).
severity	string	low, medium, high, critical.
label	string	fraudulent or benign.
label_confidence	double	0–1. Lower on ambiguous scenarios (false-positive class skews lower).

identity_context — struct

Synthetic account-level context.

Field	Type	Notes
account_id	string	Synthetic account identifier (e.g., ACCT-A1B2C3D4E5F6).
baseline_risk_score	double	0–1. Pre-sequence risk signal.
session_entropy	double	0–1. Unpredictability of session behavior.
account_age_days	int	Account age (days).
kyc_tier	string	tier_1, tier_2, tier_3.

telemetry_stream — list

Ordered financial telemetry events. One struct per event.

Event struct:

Field	Type	Notes
timestamp	string (ISO-8601)	Event time.
event_name	string	Event label (scenario-specific, e.g., LARGE_WITHDRAWAL, LIMIT_ORDER_CANCELLED, LOGIN_NEW_COUNTRY, PASSWORD_CHANGE, OPPOSITE_SIDE_FILL, VACATION_PAYMENT).
asset	string	BTC, ETH, USDC, USDT, SOL, USD, EUR, GBP, JPY.
transaction_amount_usd	double	USD-equivalent transaction amount.
geo_country	string	ISO-2 country code.
latency_ms	int	Observed latency for the event.
burst_indicator	bool	true during high-frequency burst phase.

vulnerability — struct

Scenario classification and exposure vector.

Field	Type	Notes
fraud_class	string	Account_Takeover_Sequence, Market_Manipulation_Spoofing, Legitimate_High_Risk_Activity.
scenario_description	string	Short English description of the scenario.
exposure_vector	string	Primary exposure dimension (e.g., credential_abuse, order_book_spoof, geo_anomaly, rapid_burst, session_hijack, benign_travel_pattern).

detection — struct

Anomaly-signature metadata describing how this sequence would be detected in production.

Field	Type	Notes
anomaly_type	string	One of: temporal_burst_sequence, cancel_burst_with_opposite_fill, geo_travel_with_elevated_spend.
baseline_deviation	string	Short English description of the deviation pattern (includes burst count and geo diversity).
detection_logic	string	SQL-like rule expression showing the production-style detector.
anomaly_score	double	0–1.
confidence_band	string	low / medium / high / very_high.

impact — struct

Financial exposure attributable to the sequence.

Field	Type	Notes
financial_exposure_usd	double	USD exposure during the burst (scaled down for benign sequences).
customer_funds_at_risk_usd	double	USD exposure to customer funds specifically.
recoverable_pct	double	0–1. Estimated recoverable fraction. Lower for account takeovers.

simulation — struct

Simulation engine provenance and ground-truth label.

Field	Type	Notes
synthetic	bool	Always true.
engine	string	Simulation engine label (dark_pool_sim_v1).
chaos_profile	string	Temporal_Burst_Mode, Geo_Travel_Mode, Market_Stress_Mode, Calm_Baseline, Holiday_Season_Spike.
ground_truth_classification	string	fraudulent or benign. Matches event.label.

Distribution of this sample

• 10,000 sequences total.
• Fraud class: balanced 3,333 per class across 3 classes (account takeover, spoofing, false-positive).
• Label: 2/3 fraudulent, 1/3 benign (by design — only the false-positive class is benign).
• Severity: scenario-weighted so spoofing skews mid, ATO skews high, false-positive skews low.
• Anomaly types: each fraud class maps to its own anomaly type.

Sanitization notes

• Event IDs are synthetic (DARKPOOL-*).
• Account IDs are UUID-derived synthetic identifiers (ACCT-A1B2C3...).
• No real customer data, real transactions, real wallet addresses, or PII is present.
• detection_logic is illustrative SQL-style rule syntax; it is not connected to any real detection system.
• Country codes are used as generic geo labels, not as allegations about any specific jurisdiction's transaction profile.

Relationship to the full pack

The production pack scales to 5M+ sequences with expanded scenario coverage (layering, smurfing, wash trading, sanctions-evasion routing, trade-based ML, bust-out fraud), additional asset classes (equities, FX, derivatives, NFTs), multi-entity transaction graphs, richer ambiguous-label distribution tuning, and SAR-narrative-aligned delivery. See the pack card for commercial access.