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license: other |
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# DBbun Crypto Synthetic Dataset |
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**DBbun Crypto Synthetic** is a large-scale, privacy-safe simulation of blockchain-style transactions. |
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The dataset was **inspired by the paper**: |
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*“Beyond Static Datasets: A Behavior-Driven Entity-Specific Simulation to Overcome Data Scarcity and Train Effective Crypto Anti-Money Laundering Models.”* |
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No data were copied or extracted from that study. Instead, DBbun recreated its behavioral logic and structural principles to generate fully artificial yet statistically realistic records. |
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## Summary of the Generated Dataset |
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| Table | Approx. Rows | Description | |
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|--------|---------------|-------------| |
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| **transactions.csv** | ~500,000 | One synthetic transaction per row | |
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| **edges.csv** | ~10–15 million | Sender → receiver edges forming the transaction graph | |
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| **accounts.csv** | ~100,000 | Unique wallets and entities | |
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| **labels_entities.csv** | ~100,000 | Entity-level labels (`licit / illicit` + K-hop proximity) | |
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| **labels_transactions.csv** | ~500,000 | Transaction-level labels (`benign / suspicious` + heuristics) | |
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| **stats.json** | — | Summary counts and distributions | |
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## Files Included |
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| File | Description | Rows (approx.) | |
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|------|--------------|----------------| |
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| `transactions.csv` | One row per synthetic transaction with timestamp, pattern, and fee. | 500K | |
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| `edges.csv` | Directed sender → receiver edges forming the transaction graph. | 10–15M | |
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| `accounts.csv` | Wallet-level entities with balances, lifetimes, and address counts. | 100K | |
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| `labels_entities.csv` | Entity labels (`licit / illicit`) + graph proximity (K-hop). | 100K | |
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| `labels_transactions.csv` | Transaction labels (`benign / suspicious`) + heuristic flags. | 500K | |
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| `stats.json` | Summary statistics and distributions. | — | |
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Each table is self-contained and can be joined using common keys such as `tx_id` (for transaction-level joins) and `account_id` (for entity-level joins). |
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## Schema Overview |
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### Transactions |
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Each row represents a synthetic transaction in a blockchain-style ledger. |
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| Column | Description | |
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|--------|--------------| |
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| `tx_id` | Unique transaction identifier | |
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| `timestamp` | UTC datetime | |
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| `pattern` | Transaction behavior type (`regular`, `mixer`, `coinjoin`, `exchange_withdraw`, `fan_out`, `peel_chain`, `single_use`) | |
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| `num_inputs` | Number of input addresses | |
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| `num_outputs` | Number of output addresses | |
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| `total_in` | Total input amount | |
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| `total_out` | Total output amount | |
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| `fee` | Transaction fee | |
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| `tx_hash` | SHA-256 hash (deterministic and reproducible) | |
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### Edges |
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Directed sender → receiver relationships that form the transaction graph. |
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| Column | Description | |
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|--------|--------------| |
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| `tx_id` | Transaction identifier | |
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| `timestamp` | UTC datetime | |
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| `sender` | Sending wallet or address | |
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| `receiver` | Receiving wallet or address | |
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| `value` | Amount transferred | |
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| `pattern` | Transaction behavior pattern | |
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### Accounts |
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Wallet-level entity table representing participants in the system. |
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| Column | Description | |
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|--------|--------------| |
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| `account_id` | Unique wallet identifier | |
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| `entity_type` | Category (e.g., `exchange`, `mixer`, `mule`, `business`, `service`, `licit`, `nested`) | |
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| `first_seen` | Earliest activity timestamp | |
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| `last_seen` | Most recent activity timestamp | |
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| `current_balance` | Remaining balance | |
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| `n_addresses` | Number of associated addresses | |
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--- |
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### Labels — Entities |
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Entity-level labels describing risk, type, and graph proximity. |
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| Column | Description | |
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|--------|--------------| |
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| `account_id` | Wallet identifier | |
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| `entity_type` | Same as in accounts | |
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| `entity_label` | `licit` or `illicit` | |
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| `k_hop_dist` | Integer distance from an illicit entity | |
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| `k_hop_label` | Categorical proximity flag (`within_2hop`, `far`) | |
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### Labels — Transactions |
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Labels and heuristics associated with each transaction. |
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| Column | Description | |
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|--------|--------------| |
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| `tx_id` | Transaction identifier | |
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| `pattern` | Behavioral pattern | |
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| `tx_label` | `benign` or `suspicious` | |
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| `is_fan_in` | True if many inputs converge | |
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| `is_fan_out` | True if one input splits into many | |
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| `is_roundish` | True if rounded amounts observed | |
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| `is_bursty_hour` | True if occurs during a local burst hour | |
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| `is_bursty_day` | True if part of a burst day | |
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## Use Cases |
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- **Graph Analytics** — explore transaction flows, community detection, and centrality. |
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- **Machine Learning** — train models for illicit-activity or suspicious-transaction detection. |
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- **Education** — teach blockchain analytics and anti-money-laundering frameworks. |
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- **Benchmarking** — stress-test ETL, graph databases, and networ |
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