129 research outputs found
Short Paper: An Exploration of Code Diversity in the Cryptocurrency Landscape
Interest in cryptocurrencies has skyrocketed since their introduction a decade ago, with hundreds of billions of dollars now invested across a landscape of thousands of different cryptocurrencies. While there is significant diversity, there is also a significant number of scams as people seek to exploit the current popularity. In this paper, we seek to identify the extent of innovation in the cryptocurrency landscape using the open-source repositories associated with each one. Among other findings, we observe that while many cryptocurrencies are largely unchanged copies of Bitcoin, the use of Ethereum as a platform has enabled the deployment of cryptocurrencies with more diverse functionalities
Detection of illicit accounts over the Ethereum blockchain
The recent technological advent of cryptocurrencies and their respective benefits have been shrouded with a number of illegal activities operating over the network such as money laundering, bribery, phishing, fraud, among others. In this work we focus on the Ethereum network, which has seen over 400 million transactions since its inception. Using 2179 accounts flagged by the Ethereum community for their illegal activity coupled with 2502 normal accounts, we seek to detect illicit accounts based on their transaction history using the XGBoost classifier. Using 10 fold cross-validation, XGBoost achieved an average accuracy of 0.963 ( ± 0.006) with an average AUC of 0.994 ( ± 0.0007). The top three features with the largest impact on the final model output were established to be ‘Time diff between first and last (Mins)’, ‘Total Ether balance’ and ‘Min value received’. Based on the results we conclude that the proposed approach is highly effective in detecting illicit accounts over the Ethereum network. Our contribution is multi-faceted; firstly, we propose an effective method to detect illicit accounts over the Ethereum network; secondly, we provide insights about the most important features; and thirdly, we publish the compiled data set as a benchmark for future related works
Why Trick Me: The Honeypot Traps on Decentralized Exchanges
Decentralized Exchanges (DEXs) are one of the most important infrastructures
in the world of Decentralized Finance (DeFi) and are generally considered more
reliable than centralized exchanges (CEXs). However, some well-known
decentralized exchanges (e.g., Uniswap) allow the deployment of any unaudited
ERC20 tokens, resulting in the creation of numerous honeypot traps designed to
steal traders' assets: traders can exchange valuable assets (e.g., ETH) for
fraudulent tokens in liquidity pools but are unable to exchange them back for
the original assets.
In this paper, we introduce honeypot traps on decentralized exchanges and
provide a taxonomy for these traps according to the attack effect. For
different types of traps, we design a detection scheme based on historical data
analysis and transaction simulation. We randomly select 10,000 pools from
Uniswap V2 \& V3, and then utilize our method to check these pools.Finally, we
discover 8,443 abnormal pools, which shows that honeypot traps may exist widely
in exchanges like Uniswap. Furthermore, we discuss possible mitigation and
defense strategies to protect traders' assets
Disentangling Decentralized Finance (DeFi) Compositions
We present the first study on compositions of Decentralized Finance (DeFi)
protocols, which aim to disrupt traditional finance and offer financial
services on top of the distributed ledgers, such as the Ethereum. Starting from
a ground-truth of 23 DeFi protocols and 10,663,881 associated accounts, we
study the interactions of DeFi protocols and associated smart contracts from a
macroscopic perspective. We find that DEX and lending protocols have a high
degree centrality, that interactions among protocols primarily occur in a
strongly connected component, and that known community detection cannot
disentangle DeFi protocols. Therefore, we propose an algorithm for extracting
the building blocks and uncovering the compositions of DeFi protocols. We apply
the algorithm and conduct an empirical analysis finding that swaps are the most
frequent building blocks and that DeFi aggregation protocols utilize functions
of many other DeFi protocols. Overall, our results and methods contribute to a
better understanding of a new family of financial products and could play an
essential role in assessing systemic risks if DeFi continues to proliferate
- …