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7fc0764 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | # 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<struct>
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.
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