LookAhead: Preventing DeFi Attacks via Unveiling Adversarial Contracts

• URL: http://arxiv.org/abs/2401.07261v4
• Date: Wed, 18 Sep 2024 19:29:39 GMT
• Title: LookAhead: Preventing DeFi Attacks via Unveiling Adversarial Contracts
• Authors: Shoupeng Ren, Lipeng He, Tianyu Tu, Di Wu, Jian Liu, Kui Ren, Chun Chen,
• Abstract summary: Decentralized Finance (DeFi) incidents have resulted in financial damages exceeding 3 billion US dollars. Current detection tools face significant challenges in identifying attack activities effectively. We propose a new direction for detecting DeFi attacks that focuses on identifying adversarial contracts.
• Score: 15.071155232677643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decentralized Finance (DeFi) incidents stemming from the exploitation of smart contract vulnerabilities have culminated in financial damages exceeding 3 billion US dollars. Existing defense mechanisms typically focus on detecting and reacting to malicious transactions executed by attackers that target victim contracts. However, with the emergence of private transaction pools where transactions are sent directly to miners without first appearing in public mempools, current detection tools face significant challenges in identifying attack activities effectively. Based on the fact that most attack logic rely on deploying one or more intermediate smart contracts as supporting components to the exploitation of victim contracts, in this paper, we propose a new direction for detecting DeFi attacks that focuses on identifying adversarial contracts instead of adversarial transactions. Our approach allows us to leverage common attack patterns, code semantics and intrinsic characteristics found in malicious smart contracts to build the LookAhead system based on Machine Learning (ML) classifiers and a transformer model that is able to effectively distinguish adversarial contracts from benign ones, and make just-in-time predictions of potential zero-day attacks. Our contributions are three-fold: First, we construct a comprehensive dataset consisting of features extracted and constructed from recent contracts deployed on the Ethereum and BSC blockchains. Secondly, we design a condensed representation of smart contract programs called Pruned Semantic-Control Flow Tokenization (PSCFT) and use it to train a combination of ML models that understand the behaviour of malicious codes based on function calls, control flows and other pattern-conforming features. Lastly, we provide the complete implementation of LookAhead and the evaluation of its performance metrics for detecting adversarial contracts.

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