LookAhead: Preventing DeFi Attacks via Unveiling Adversarial Contracts

• URL: http://arxiv.org/abs/2401.07261v5
• Date: Wed, 02 Apr 2025 02:00:44 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.<n>Current detection tools face significant challenges in identifying attack activities effectively.<n>We propose a new framework for effectively detecting DeFi attacks via unveiling adversarial contracts.
• Score: 15.071155232677643
• License: http://creativecommons.org/licenses/by/4.0/
• 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, detection methods have been proposed that focus on identifying these adversarial contracts instead of adversarial transactions. However, previous state-of-the-art approaches in this direction have failed to produce results satisfactory enough for real-world deployment. In this paper, we propose a new framework for effectively detecting DeFi attacks via unveiling adversarial contracts. 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 timely predictions of different types of potential attacks. Experiments show that LookAhead achieves an F1-score as high as 0.8966, which represents an improvement of over 44.4% compared to the previous state-of-the-art solution Forta, with a False Positive Rate (FPR) at only 0.16%.

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