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On-Chain Behavioral Signatures for Classifying Centralized Exchange Addresses on Ethereum Using Proof-of-Reserves — arXiv:2610.41888

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41888
License (as archived): CC0-1.0
Archived: 2026-11-03T07:57:38.743Z
SHA-256 of the archived text: aead8ddaa66bc9e20eb6e0086a574ccac1c67691b6a66dc6f9be5cc39c6b75f2
Stable URL of this page: https://metraipolis.com/documento/dc_40428efb44bf

How to cite

On-Chain Behavioral Signatures for Classifying Centralized Exchange Addresses on Ethereum Using Proof-of-Reserves — arXiv:2610.41888 — arXiv (en), https://arxiv.org/abs/2610.41888 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_40428efb44bf (Metraipolis archive dc_40428efb44bf, sha256 aead8ddaa66bc9e20eb6e0086a574ccac1c67691b6a66dc6f9be5cc39c6b75f2)

BibTeX

@misc{dc40428efb44bf,
  title = {On-Chain Behavioral Signatures for Classifying Centralized Exchange Addresses on Ethereum Using Proof-of-Reserves — arXiv:2610.41888},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41888},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_40428efb44bf; sha256 aead8ddaa66bc9e20eb6e0086a574ccac1c67691b6a66dc6f9be5cc39c6b75f2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41888. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Centralized exchanges (CEXs) are key gateways between traditional finance and digital asset markets, yet identifying their blockchain addresses remains challenging. Existing approaches often rely on opaque attribution labels or treat exchanges as secondary classification targets, leaving generalization to unseen exchanges insufficiently examined. This study develops a classification framework using Proof-of-Reserves (PoR) disclosures, with 3,033,857 Ethereum addresses from 15 CEXs and five Non-CEX categories. We derive 58 on-chain behavioral features and evaluate LightGBM against logistic regression (LR) under a leave-one-exchange-out protocol. Across five lookback windows (30-730 days), LightGBM consistently outperformed LR, with macro PR-AUC increasing from 0.6611 to 0.9594 using the selected 17 features, compared with 0.7697 for LR at 730 days. Robustness checks showed comparable performance when feature selection and hyperparameter optimization were repeated across splits and held-out exchanges, while exchange-balanced reweighting improved macro PR-AUC. SHAP analysis and feature-group ablation identified gas behavior, ERC-20 token activity, and internal transactions as key predictive signals in the constructed dataset. These findings support interpretable CEX address screening for blockchain monitoring, while the released feature dataset enables reproducible follow-up research.

Tema degli esploratori: governance digitale e beni comuni (elenco arXiv cs.CY, nuovi lavori del giorno).
Categorie arXiv: Computers and Society (cs.CY) ; Cryptography and Security (cs.CR); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.41888 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:50:45.941Z dalla pagina https://arxiv.org/list/cs.CY/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).