Demystifying DeFi MEV Activities in Flashbots Bundle
- URL: http://arxiv.org/abs/2312.01091v1
- Date: Sat, 2 Dec 2023 09:46:39 GMT
- Title: Demystifying DeFi MEV Activities in Flashbots Bundle
- Authors: Zihao Li, Jianfeng Li, Zheyuan He, Xiapu Luo, Ting Wang, Xiaoze Ni, Wenwu Yang, Xi Chen, Ting Chen,
- Abstract summary: Decentralized Finance, mushrooming in permissionless blockchains, has attracted a recent surge in popularity.
Due to the transparency of permissionless blockchains, opportunistic traders can compete to earn revenue by extracting Miner Extractable Value (MEV)
The Flashbots bundle mechanism further aggravates the MEV competition because it empowers opportunistic traders with the capability of designing more sophisticated MEV extraction.
- Score: 36.64508078443365
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Decentralized Finance, mushrooming in permissionless blockchains, has attracted a recent surge in popularity. Due to the transparency of permissionless blockchains, opportunistic traders can compete to earn revenue by extracting Miner Extractable Value (MEV), which undermines both the consensus security and efficiency of blockchain systems. The Flashbots bundle mechanism further aggravates the MEV competition because it empowers opportunistic traders with the capability of designing more sophisticated MEV extraction. In this paper, we conduct the first systematic study on DeFi MEV activities in Flashbots bundle by developing ActLifter, a novel automated tool for accurately identifying DeFi actions in transactions of each bundle, and ActCluster, a new approach that leverages iterative clustering to facilitate us to discover known/unknown DeFi MEV activities. Extensive experimental results show that ActLifter can achieve nearly 100% precision and recall in DeFi action identification, significantly outperforming state-of-the-art techniques. Moreover, with the help of ActCluster, we obtain many new observations and discover 17 new kinds of DeFi MEV activities, which occur in 53.12% of bundles but have not been reported in existing studies
Related papers
- Maximal Extractable Value Mitigation Approaches in Ethereum and Layer-2 Chains: A Comprehensive Survey [1.2453219864236247]
MEV arises when miners or validators manipulate transaction ordering to extract additional value.
This not only affects user experience by introducing unpredictability and potential financial losses but also threatens the underlying principles of decentralization and trust.
This paper presents a comprehensive survey of MEV mitigation techniques as applied to both protocolss L1 and various L2 solutions.
arXiv Detail & Related papers (2024-07-28T19:51:22Z) - Remeasuring the Arbitrage and Sandwich Attacks of Maximal Extractable Value in Ethereum [7.381773144616746]
Maximal Extractable Value (MEV) drives the prosperity of the blockchain ecosystem.
We propose a profitability identification algorithm to identify MEV activities on our collected largest-ever dataset.
We have characterized the overall landscape of the MEV ecosystem, the impact the private transaction architectures bring in, and the adoption of back-running mechanisms.
arXiv Detail & Related papers (2024-05-28T08:17:15Z) - Rolling in the Shadows: Analyzing the Extraction of MEV Across Layer-2 Rollups [13.27494645366702]
Decentralized finance embraces a series of exploitative economic practices known as Maximal Extractable Value (MEV)
In this paper, we investigate the prevalence and impact of MEV on prominent rollups such as Arbitrum, and zkSync over a nearly three-year period.
While our findings did not detect any sandwiching activity on popular rollups, we did identify the potential for cross-layer sandwich attacks.
arXiv Detail & Related papers (2024-04-30T18:34:32Z) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - Playing the MEV Game on a First-Come-First-Served Blockchain [2.9942612239708826]
This paper illustrates the dynamics of the MEV extraction game in an FCFS network, specifically Algorand.
We introduce an arbitrage detection algorithm tailored to the unique time constraints of FCFS networks.
Our algorithm's performance under varying time constraints underscores the importance of timing in arbitrage discovery.
arXiv Detail & Related papers (2024-01-15T22:34:00Z) - Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
This paper proposes an effective algorithm based on neural networks to take advantage of these investment products.
A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio.
A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
arXiv Detail & Related papers (2023-10-02T12:33:28Z) - Cross-modal Orthogonal High-rank Augmentation for RGB-Event
Transformer-trackers [58.802352477207094]
We explore the great potential of a pre-trained vision Transformer (ViT) to bridge the vast distribution gap between two modalities.
We propose a mask modeling strategy that randomly masks a specific modality of some tokens to enforce the interaction between tokens from different modalities interacting proactively.
Experiments demonstrate that our plug-and-play training augmentation techniques can significantly boost state-of-the-art one-stream and two trackersstream to a large extent in terms of both tracking precision and success rate.
arXiv Detail & Related papers (2023-07-09T08:58:47Z) - Leveraging Machine Learning for Multichain DeFi Fraud Detection [5.213509776274283]
We present a framework for extracting features from different chains, including the largest one, and it is evaluated over an extensive dataset.
Different Machine Learning methods were employed, such as XGBoost and a Neural Network for identifying fraud accounts detection interacting with DeFi.
We demonstrate that the introduction of novel DeFi-related features, significantly improves the evaluation results.
arXiv Detail & Related papers (2023-05-17T15:48:21Z) - Uniswap Liquidity Provision: An Online Learning Approach [49.145538162253594]
Decentralized Exchanges (DEXs) are new types of marketplaces leveraging technology.
One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds.
This introduces the problem of finding an optimal strategy for choosing price intervals.
We formalize this problem as an online learning problem with non-stochastic rewards.
arXiv Detail & Related papers (2023-02-01T17:21:40Z) - ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep
Neural Network and Transfer Learning [80.85273827468063]
Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable.
We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts.
We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
arXiv Detail & Related papers (2021-03-23T15:04:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.