DBbun Crypto Synthetic Dataset
DBbun Crypto Synthetic is a large-scale, privacy-safe simulation of blockchain-style transactions.
The dataset was inspired by the paper: “Beyond Static Datasets: A Behavior-Driven Entity-Specific Simulation to Overcome Data Scarcity and Train Effective Crypto Anti-Money Laundering Models.”
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.
Summary of the Generated Dataset
| Table | Approx. Rows | Description |
|---|---|---|
| transactions.csv | ~500,000 | One synthetic transaction per row |
| edges.csv | ~10–15 million | Sender → receiver edges forming the transaction graph |
| accounts.csv | ~100,000 | Unique wallets and entities |
| labels_entities.csv | ~100,000 | Entity-level labels (licit / illicit + K-hop proximity) |
| labels_transactions.csv | ~500,000 | Transaction-level labels (benign / suspicious + heuristics) |
| stats.json | — | Summary counts and distributions |
Files Included
| File | Description | Rows (approx.) |
|---|---|---|
transactions.csv |
One row per synthetic transaction with timestamp, pattern, and fee. | 500K |
edges.csv |
Directed sender → receiver edges forming the transaction graph. | 10–15M |
accounts.csv |
Wallet-level entities with balances, lifetimes, and address counts. | 100K |
labels_entities.csv |
Entity labels (licit / illicit) + graph proximity (K-hop). |
100K |
labels_transactions.csv |
Transaction labels (benign / suspicious) + heuristic flags. |
500K |
stats.json |
Summary statistics and distributions. | — |
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).
Schema Overview
Transactions
Each row represents a synthetic transaction in a blockchain-style ledger.
| Column | Description |
|---|---|
tx_id |
Unique transaction identifier |
timestamp |
UTC datetime |
pattern |
Transaction behavior type (regular, mixer, coinjoin, exchange_withdraw, fan_out, peel_chain, single_use) |
num_inputs |
Number of input addresses |
num_outputs |
Number of output addresses |
total_in |
Total input amount |
total_out |
Total output amount |
fee |
Transaction fee |
tx_hash |
SHA-256 hash (deterministic and reproducible) |
Edges
Directed sender → receiver relationships that form the transaction graph.
| Column | Description |
|---|---|
tx_id |
Transaction identifier |
timestamp |
UTC datetime |
sender |
Sending wallet or address |
receiver |
Receiving wallet or address |
value |
Amount transferred |
pattern |
Transaction behavior pattern |
Accounts
Wallet-level entity table representing participants in the system.
| Column | Description |
|---|---|
account_id |
Unique wallet identifier |
entity_type |
Category (e.g., exchange, mixer, mule, business, service, licit, nested) |
first_seen |
Earliest activity timestamp |
last_seen |
Most recent activity timestamp |
current_balance |
Remaining balance |
n_addresses |
Number of associated addresses |
Labels — Entities
Entity-level labels describing risk, type, and graph proximity.
| Column | Description |
|---|---|
account_id |
Wallet identifier |
entity_type |
Same as in accounts |
entity_label |
licit or illicit |
k_hop_dist |
Integer distance from an illicit entity |
k_hop_label |
Categorical proximity flag (within_2hop, far) |
Labels — Transactions
Labels and heuristics associated with each transaction.
| Column | Description |
|---|---|
tx_id |
Transaction identifier |
pattern |
Behavioral pattern |
tx_label |
benign or suspicious |
is_fan_in |
True if many inputs converge |
is_fan_out |
True if one input splits into many |
is_roundish |
True if rounded amounts observed |
is_bursty_hour |
True if occurs during a local burst hour |
is_bursty_day |
True if part of a burst day |
Use Cases
- Graph Analytics — explore transaction flows, community detection, and centrality.
- Machine Learning — train models for illicit-activity or suspicious-transaction detection.
- Education — teach blockchain analytics and anti-money-laundering frameworks.
- Benchmarking — stress-test ETL, graph databases, and networ
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