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This paper analyzes common attack vectors in decentralized finance (DeFi) stablecoin systems and proposes a real-time dynamic defense architecture to enhance security. By examining 12 real-world security incidents, the authors identify high-risk patterns such as reentrancy attacks and oracle manipulation, leading to the development of a multi-dimensional anomaly detection model based on the Bi-LSTM algorithm. The model achieves a classification accuracy of 96.61% and a recall rate of 97.70% for malicious attacks, demonstrating its effectiveness in real-time protection against vulnerabilities in stablecoins.
Real-time anomaly detection can achieve over 96% accuracy in identifying threats to DeFi stablecoins, potentially safeguarding billions in assets.
With the rapid evolution of the Decentralized Finance (DeFi) ecosystem, stablecoins have emerged as a critical infrastructure bridging the cryptocurrency market with traditional financial paradigms. However, stablecoin systems rely heavily on smart contracts to execute automated operations. The immutable nature of these systems post-deployment means that the exploitation of security vulnerabilities can lead to irreversible, massive economic losses and potentially trigger systemic financial risks. Current research on stablecoin smart contract security faces challenges such as a lack of domain-specific targeting and the obsolescence of static defense models. To address this, this paper systematically analyzes common attack vectors in stablecoin environments and proposes a practical, real-time dynamic defense architecture. By analyzing 12 real-world security incidents, we elucidate the underlying mechanisms of high-risk patterns such as reentrancy attacks, oracle manipulation, and composite flash loan attacks. Concurrently, we construct a real-time anomaly detection model utilizing multi-dimensional on-chain temporal features and the Bi-LSTM algorithm. Experimental results demonstrate that this model achieves a classification accuracy of 96.61\%, with an average recall rate of 97.70\% for malicious attack samples, and a single inference latency ranging from 1.5 to 2.8 milliseconds.
