Datasets:

enes0o0
/

cross-chain-sybil-detection

wallet_address string	is_sybil_attacker int64	bridgeTransactions_arbitrum_from_count float64	bridgeTransactions_arbitrum_from_amount float64	bridgeTransactions_polygon_from_count float64	bridgeTransactions_polygon_from_amount float64	bridgeTransactions_mainnet_from_count float64	bridgeTransactions_optimism_from_count float64	bridgeTransactions_optimism_from_amount float64	bridgeTransactions_xdai_from_count float64	bridgeTransactions_xdai_from_amount float64	bridgeTransactions_mainnet_to_count float64	total_transactions_from float64	total_transactions_to float64	total_amount_from float64	airdrop_amount float64	risk_score_ float64	activity_span_ float64	hour_entropy_ float64	activity_bursts_ float64	cluster_risk_ float64	cluster_size_ float64	avg_transaction_amount float64	network_diversity float64	transaction_ratio float64	amount_ratio float64	cotemp_burst_count_60s float64	cotemp_peer_total_60s float64	cotemp_max_burst_size_60s float64	cotemp_chains_used_60s float64	cotemp_event_count_60s float64	cotemp_density_60s float64	cotemp_unique_partners_60s float64	cotemp_burst_count_300s float64	cotemp_peer_total_300s float64	cotemp_max_burst_size_300s float64	cotemp_chains_used_300s float64	cotemp_event_count_300s float64	cotemp_density_300s float64	cotemp_unique_partners_300s float64	funding_in_degree float64	funding_out_degree float64	funding_in_count float64	funding_out_count float64	funded_on_n_chains float64	funding_wcc_size float64	efc_n_memberships float64	efc_n_distinct_sessions float64	efc_n_distinct_sources float64	efc_max_cluster_size float64	efc_mean_cluster_size float64	efc_unique_clustermates float64	efc_max_risk float64	efc_mean_risk float64	efc_max_total_funding float64	efc_max_time_span float64	efc_max_n_networks float64	efc_max_n_tokens float64	efc_max_amt_variance float64
0x00000000000a29a0800f6f557ddbbe8249397de7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	5,646,690,842,428,825,000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
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0x00000000005dbcb0d0513fcda746382fe8a53468	0	1	645,563,000,636,487,000	1	2,314,824,216,820,036,000,000	0	0	0	0	0	1	2	1	2,315,469,779,820,672,500,000	0	0.25	123.26	1.4949	2	0.5	202	771,823,259,940,224,100,000	2	1	2,315,469,779,820,672,500,000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	202	202	201	0.5	0.5	5,119,493,006,310,695,000,000,000	335.98	1	1	1,044,812,644,478,433,000,000,000,000,000,000,000,000,000,000
0x00000000005ef87f8ca7014309ece7260bbcdaeb	0	0	0	0	0	0	0	0	0	0	0	0	0	0	594,433,476,191,075,000,000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
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Cross-Chain Sybil Attack Detection Dataset

A large-scale, labeled dataset for detecting Sybil attacks in blockchain networks using cross-chain behavioral modeling.

Dataset Description

This dataset contains 196,477 wallet addresses collected from five blockchain networks (Ethereum, Polygon, Arbitrum, Optimism, and xDai) with 60 engineered features and binary Sybil labels. It was constructed to support research on automated Sybil wallet detection in decentralized finance (DeFi) ecosystems.

Ground-truth Sybil labels were obtained from the Hop Protocol airdrop campaign, verified through clustering analysis, temporal pattern inspection, and funding source tracking.

Key Statistics

Metric	Value
Total wallets	196,477
Sybil wallets	14,195 (7.22%)
Normal wallets	182,282 (92.78%)
Features	60
Columns	62 (wallet_address + is_sybil_attacker + 60 features)
Blockchain networks	Ethereum, Polygon, Arbitrum, Optimism, xDai

Class Distribution

The dataset exhibits significant class imbalance (~7.22% Sybil), which is representative of real-world fraud detection scenarios.

Features

The 60 features are organized into the following groups:

Feature Group	Count	Description
Bridge Transaction	10	Cross-chain bridge usage counts and amounts per network
Aggregate	4	Total transaction counts, amounts, and airdrop received
Behavioral / Enhanced	6	Risk score, activity span, hour entropy, activity bursts, cluster info
Derived	4	Computed ratios: avg_transaction_amount, network_diversity, transaction_ratio, amount_ratio
Co-Temporal Graph (60s)	7	Burst coordination metrics within 60-second windows
Co-Temporal Graph (300s)	7	Burst coordination metrics within 300-second windows
Funding Graph	9	Funding source topology: in/out degree, sibling wallets, WCC size
Entity-Funding Cluster (EFC)	13	Cluster-level aggregates: size, risk, funding volume, diversity

Non-Feature Columns

Column	Description
`wallet_address`	Unique wallet identifier
`is_sybil_attacker`	Binary label: 1 = Sybil, 0 = Normal

Usage

from datasets import load_dataset

dataset = load_dataset("enes0o0/cross-chain-sybil-detection")
df = dataset["train"].to_pandas()

X = df.drop(columns=["wallet_address", "is_sybil_attacker"])
y = df["is_sybil_attacker"]

Benchmark Results

Models evaluated using 10x random train/test splits (80/20) with hyperparameter optimization:

Model	AUC-ROC	F1-Score	Type
LightGBM	0.9350 ± 0.0021	0.6164 ± 0.0087	Supervised (Boosting)
CatBoost	0.9327 ± 0.0013	0.5937 ± 0.0066	Supervised (Boosting)
RandomForest	0.9300 ± 0.0024	0.5756 ± 0.0040	Supervised (Bagging)
TabNet	0.8739 ± 0.0140	0.3978 ± 0.0324	Supervised (Deep Tabular)
IsolationForest	0.6940 ± 0.0043	0.1712 ± 0.0116	Unsupervised
OneClassSVM	0.5471 ± 0.0122	0.2185 ± 0.0060	Unsupervised

Associated Paper

An Interpretable and Robust Machine Learning Framework for Sybil Attack Detection Based on Cross-Chain Behavioral Modeling

Berke Gunes (BIS Cozum Bilgisayar ve Entegrasyon Hizmetleri ve Tic. A.S, Istanbul), Enes Ozgozler (Bahcesehir University, Dept. of Computer Engineering, Istanbul), C. Okan Sakar (Bahcesehir University, Dept. of Computer Engineering, Istanbul)

This dataset is an extended version of the data used in the conference paper presented at MedPRAI 2026, expanded with co-temporal burst, funding graph, and entity-funding cluster features.

Citation

If you use this dataset, please cite the associated conference paper:

Gunes, B., Sakar, C.O.: Cross-chain behavioral modeling for sybil attack detection in blockchain networks. In: 7th Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI) (2026)

@inproceedings{gunes2026medprai,
  title={Cross-Chain Behavioral Modeling for Sybil Attack Detection in Blockchain Networks},
  author={Gunes, B. and Sakar, C.O.},
  booktitle={7th Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI)},
  year={2026}
}

License

MIT

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