| # Aestrea Dark Pool Financial Anomaly Pack — Schema |
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| One row = one financial activity sequence (warm-up → burst). All records share the same seven top-level fields. |
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| Schema version: `1.0.0-dark-pool-sample` |
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| ## Top-level fields |
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| ### `schema_version` — string |
| Schema identifier. Constant within a sample release. |
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| ### `event` — struct |
| Identifier fields and the overall label/severity for the sequence. |
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| | Field | Type | Notes | |
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| | `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). | |
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| ### `identity_context` — struct |
| Synthetic account-level context. |
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| | Field | Type | Notes | |
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| | `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`. | |
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| ### `telemetry_stream` — list<struct> |
| Ordered financial telemetry events. One struct per event. |
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| Event struct: |
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| | Field | Type | Notes | |
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| | `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. | |
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| ### `vulnerability` — struct |
| Scenario classification and exposure vector. |
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| | Field | Type | Notes | |
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| | `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`). | |
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| ### `detection` — struct |
| Anomaly-signature metadata describing how this sequence would be detected in production. |
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| | Field | Type | Notes | |
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| | `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`. | |
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| ### `impact` — struct |
| Financial exposure attributable to the sequence. |
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| | Field | Type | Notes | |
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| | `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. | |
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| ### `simulation` — struct |
| Simulation engine provenance and ground-truth label. |
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| | Field | Type | Notes | |
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| | `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`. | |
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| ## Distribution of this sample |
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| - 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. |
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| ## Sanitization notes |
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| - 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. |
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| ## Relationship to the full pack |
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| 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. |
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