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This paper analyzes vulnerabilities in DeFi stablecoins, focusing on attack vectors like reentrancy and oracle manipulation, and proposes a real-time dynamic defense architecture to enhance security. By leveraging multi-dimensional on-chain temporal features and a Bi-LSTM algorithm, the authors develop an anomaly detection model that achieves a classification accuracy of 96.61% and a recall rate of 97.70% for malicious attacks. These findings highlight the critical need for adaptive security measures in immutable smart contract systems to prevent significant economic losses and systemic risks.
Real-time anomaly detection can mitigate the risk of catastrophic failures in DeFi stablecoins, achieving over 96% accuracy in identifying threats.
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.