相关论文: How To Cook The Fragmented Rug Pull?

NFT rug pull is one of the most prominent type of scam that the developers of a project abandon it and then run away with investors' funds. Although they have drawn attention from our community, to the best of our knowledge, the NFT rug…

The explosive growth of non-fungible tokens (NFTs) on Web3 has created a new frontier for digital art and collectibles, but also an emerging space for fraudulent activities. This study provides an in-depth analysis of NFT rug pulls, which…

Smart contract-based ecosystems enable decentralized applications without trusted intermediaries, but their immutability and permissionless design also facilitate large-scale fraud. One of the most prevalent attacks is the rug pull, where…

Rug pull scams have emerged as a persistent threat to cryptocurrency, causing significant financial losses. A typical scenario involves scammers deploying honeypot contracts to attract investments, restricting token sales, and draining the…

Rug pulls pose a grave threat to the cryptocurrency ecosystem, leading to substantial financial loss and undermining trust in decentralized finance (DeFi) projects. With the emergence of new rug pull patterns, research on rug pull is out of…

Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi). A rug pull occurs when developers exploit users' trust and drain liquidity from token pools on Decentralized Exchanges (DEXs),…

The explosive growth of Non-Fungible Tokens (NFTs) has revolutionized digital ownership by enabling the creation, exchange, and monetization of unique assets on blockchain networks. However, this surge in popularity has also given rise to a…

We explored the ubiquitous phenomenon of serial scammers, each of whom deployed dozens to thousands of addresses to conduct a series of similar Rug Pulls on popular decentralized exchanges. We first constructed two datasets of around…

Rug pull is a critical attack in the world of blockchain technology. Despite this, the absence of sufficient time-bound and well-structured datasets is considered one of the significant issues faced while identifying early detection.…

This paper presents a machine learning framework for the early detection of rug pull scams on decentralized exchanges (DEXs) within The Open Network (TON) blockchain. TON's unique architecture, characterized by asynchronous execution and a…

In recent years, Decentralized Finance (DeFi) grows rapidly due to the development of blockchain technology and smart contracts. As of March 2023, the estimated global cryptocurrency market cap has reached approximately $949 billion.…

Uniswap, like other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of…

Solana has experienced rapid growth due to its high performance and low transaction costs, but the extremely low barrier to token issuance has also led to widespread Rug Pulls. Unlike Ethereum-based Rug Pulls that rely on malicious smart…

We identify the slow liquidity drain (SLID) scam, an insidious and highly profitable threat to decentralized finance (DeFi), posing a large-scale, persistent, and growing risk to the ecosystem. Unlike traditional scams such as rug pulls or…

Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security. Due to model complexity, network unreliability and connection…

Financial fraud is the cause of multi-billion dollar losses annually. Traditionally, fraud detection systems rely on rules due to their transparency and interpretability, key features in domains where decisions need to be explained.…

The emergence of Concentrated Liquidity Market Makers (CLMMs) has made liquidity provision on decentralized exchanges an active and risk-sensitive task. However, the standalone profitability of liquidity provision remains unclear for…

In this work, we perform a longitudinal analysis of the BNB Smart Chain and Ethereum blockchain from their inception to March 2022. We study the ecosystem of the tokens and liquidity pools, highlighting analogies and differences between the…

Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks…

This paper studies pipelined algorithms for protecting distributed grid computations from cheating participants, who wish to be rewarded for tasks they receive but don't perform. We present improved cheater detection algorithms that utilize…