Responsible Approaches to Blockchain, Cryptocurrency, and FinTech

Permanent URI for this collectionhttps://hdl.handle.net/10125/112497

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    Hook, Line and Rug Pull: Untangling the Web of Crypto Scam Sites
    (2026-01-06) Jakobs, Jenny; Haase, Frederic; Gräf, Christian; Jan, Muntermann
    The rapid growth of cryptocurrencies has been accompanied by a rise in illicit activities within the digital asset ecosystem. Among these, rug pulls have emerged as a prevalent form of fraud in which project creators promote and sell tokens under false pretenses, attract funds, and then abruptly withdraw liquidity, leaving investors with worthless assets. This study introduces a novel detection approach based on linguistic and structural cues from these websites. Our findings reveal that legitimate cryptocurrency websites differ significantly from rug pulls in both language use and structure. Tree-based classifiers demonstrate strong potential for identifying rug pull schemes, achieving classification accuracies of over 82%. These results not only enhance fraud detection capabilities but also contribute to a deeper understanding of the characteristics and tactics behind rug pull schemes.
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    Practical Strategies for DeFi Efficiency: A Technical Framework for Arbitrage, Liquid Staking Redemption, and Yield Farming
    (2026-01-06) Schwertfeger, Lennart; Vogt, Bodo
    Decentral finance (DeFi) protocols allow blockchain users to execute various financial operations while keeping custodianship of funds. The pitfall of the current DeFi system is weak communication between protocols and blockchains, which leads to fragmented and inefficient allocated capital. The paper investigates price inefficiencies between centralized exchanges and decentralized exchanges in cryptocurrency markets and presents the design of a secure, server-based arbitrage trading system to exploit fragmented capital. We develop a mathematical framework for two-point and triangular arbitrage trading and describe the technical requirements for deploying a high-frequency trading bot using a self-hosted blockchain node. The algorithm dynamically updates cross-exchange orders by analyzing mempool data, which allows traders to disclose spot price changes at the decentralized exchange before a new block is found. Through a simulation of two trade sequences in a Uniswap automated market maker (AMM), we demonstrate how transaction ordering affects the resulting spot price, even without front-running attacks, introducing pricing uncertainty and miner extractable value (MEV) opportunities. We extend the server infrastructure to support other DeFi strategies that benefits from inefficiently allocated capital such as liquid staking redemption and yield farming strategies. The proposed technical framework enables investors to overcome structural inefficiencies in the DeFi ecosystem without relying on third-party intermediaries and offers a scalable and secure infrastructure for long-term, decentral financial automation.
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