The paper presents a classification framework that uses Proof-of-Reserves disclosures to identify centralized exchange (CEX) addresses on Ethereum. It analyzes 3,033,857 addresses from 15 CEXs and five non-CEX categories, extracting 58 on-chain behavioral features and demonstrating that LightGBM outperforms logistic regression across various lookback windows. Key predictive signals include gas behavior, ERC‑20 token activity, and internal transactions, and the authors release a feature dataset for reproducible research.
arXiv:2608. 20271v1 Announce Type: new Abstract: The rapid proliferation of memecoins on blockchain platforms has increased the risk of fraudulent activities, particularly rug pulls.
The paper presents a method for early detection of fraudulent memecoins (rug pulls) on the Solana blockchain, using a dataset of 6.4 million tokens collected over seven months. It shows that most rug pulls occur within an hour of launch and that classic machine learning models, especially Gradient Boosting (XGBoost), can reliably predict them using only the first five minutes of trading data. Cross‑platform data fusion between PumpFun and Raydium further improves detection by reducing domain shift.
arXiv:2607. 27350v1 Announce Type: new Abstract: Sybil bots are Ethereum actors that imitate legitimate users to extract airdrop rewards or influence governance.
arXiv:2609.23659v1 Announce Type: new Abstract: Ethereum decentralized finance (DeFi) provides a public, time-stamped record of transaction-level event streams, but the same public symbols can create...
FIDES-Live extends the FIDES framework to audit sequential trading agents by separating decision validity, numerical commitment adherence, execution and ledger reconciliation, and receipt‑grounded reporting. In a retrospective audit of 7,299 cryptocurrency account‑rounds and a 310‑round equity pilot, decision validity reached 97.35% and structured reporting accuracy 90.81%, while commitment adherence overall was 94.36% but dropped to 62.63% for non‑empty order proposals. The study shows that all cash and position transitions reconcile with recorded fills, and highlights the need to report concordance, evidence coverage, activity, and financial outcomes separately.
arXiv:2501.18158v4 Announce Type: replace-cross Abstract: Cryptocurrencies are widely used, yet current methods for analyzing transactions often rely on opaque, black-box models. While these models m...
DeFiFusion is a dual‑modal framework that detects price manipulation attacks in decentralized finance by jointly analyzing transaction events and smart contract semantics. It encodes fine‑grained temporal and economic features from transactions and extracts contract logic using large language models, then fuses these signals with a Dual‑Modal Projection‑Fusion Transformer. The approach achieves state‑of‑the‑art performance, recalling 222 of 225 known attacks with 96.10% precision.
arXiv:2607. 27370v1 Announce Type: new Abstract: Sybil attackers are Blockchain actors that adopt the characteristics of regular users to exploit airdrops or influence governance.
Sequential trading agents produce decisions, orders, execution receipts, account states, and reports that need not agree, even when the final portfolio value is correctly computed. We present FIDES-Li...
The paper presents a deployed system that scores blockchain addresses using their position in a massive multi‑chain transaction graph instead of relying on sanctions lists. The system operates on a single graph of 835 million addresses and 15.8 billion edges across five EVM chains, employing a shared inductive encoder with per‑chain normalization and two scoring heads. It demonstrates label‑free transfer, achieving high recall on held‑out positives for Base, Arbitrum, and Gnosis at a very low alert rate, and shows significant lead time over external registry events, while maintaining fast, reproducible serving performance.
The paper presents a compliance screening system that evaluates blockchain addresses by their position in a large multi‑chain transaction graph instead of relying on sanctions lists. Using a single graph of 835 million addresses and 15.8 billion edges across five EVM chains, the system employs a shared inductive encoder with per‑chain normalization and two scoring heads, with decision thresholds set as exact quantiles of the score distribution. The authors demonstrate label‑free transfer, achieving high recall on held‑out positives for Base, Arbitrum, and Gnosis, and report significant lead‑time in flagging external registry events, efficient serving latency, and robustness checks against adversarial behavior.
arXiv:2606. 02528v1 Announce Type: cross Abstract: Large language models now power robo-advisors and trading agents, yet whether they carry built-in biases toward specific assets is largely untested.