Defifusion improves detection of price manipulation in decentralized finance
DeFiFusion: Combining Transaction Events with Smart Contracts to Detect Price Manipulation Attacks
Cryptography and SecurityArtificial IntelligenceSoftware Engineering
Summary
Decentralized Finance (DeFi) platforms allow people to trade and manage money without middlemen, but they can be tricked by price manipulation attacks that cause big losses. Existing methods to catch these tricks either look only at transaction data or only at smart contract code, missing important clues. The authors propose DeFiFusion, which combines information from both transactions and smart contracts to better spot manipulation schemes. Their approach uses sophisticated machine learning to understand how transaction patterns interact with contract rules, leading to more accurate detection.
What this means in practice
- •For blockchain security teams: Detect and prevent price manipulation attacks by analyzing both transaction patterns and smart contract code for improved DeFi platform security.
- •For financial fraud detection teams: Identify complex blockchain-based price manipulations by combining behavioral data and contract logic analysis to reduce false positives.
Authors
Rui Cao, Shaojing Fan, Liming Fang, Yuchan Liu, Yingying Jiao, Zhenguang Liu
Abstract
Decentralized Finance (DeFi) has emerged as a rapidly growing blockchain-based financial service, where market transaction dynamics and underlying smart contract logic are intricately intertwined. This autonomous interplay, while eliminating centralized intermediaries, significantly expands the vulnerability surface of DeFi protocols to Price Manipulation Attacks (PMAs), which have already inflicted catastrophic financial losses. Despite their gravity, existing detection paradigms suffer from fundamental limitations. Transaction-centric methods lack awareness of contract execution semantics, making them prone to false positives under legitimate market volatility, while static contract analyses ignore real transaction behaviors and frequently report vulnerabilities that are infeasible to exploit in practice. We present DeFiFusion, a dual-modal PMA detection framework that closes this gap by jointly modeling transaction events and smart contract semantics within a unified pipeline. Our core insight is that PMA maliciousness emerges only from the interaction between transaction behaviors and the contract logic they exploit; neither signal suffices in isolation. Accordingly, we derive price-manipulation-aware event encoding for extracting fine-grained temporal and economic features tailored to manipulation patterns. We further introduce LLM-based contract semantic extraction to supply the execution-logic context that prior behavioral methods lack. To fuse these modalities, we propose a Dual-Modal Projection-Fusion Transformer with T5-style relative positional encoding, capturing the cyclic multi-stage execution structures that distinguish PMAs from benign market activity. Extensive experiments demonstrate that DeFiFusion consistently achieves state-of-the-art detection performance, effectively recalling 222 of the 225 PMA cases while maintaining a precision of 96.10%.