Anomaly VAE-Transformer: A Deep Learning Approach for Anomaly Detection in Decentralized Finance

DeFi, a decentralized financial service based on blockchain, not only provides innovative financial services, but also poses various risks, such as the Terra Luna crash. Therefore, anomaly detection in DeFi is necessary to ensure the safety and reliability of the DeFi ecosystem. However, this is very difficult because of the complex protocol, interaction among smart contracts, and high market volatility. In this study, we propose a novel method to effectively detect anomalies in DeFi. To the best of our knowledge, this is the first study that utilizes deep learning to detect anomalies in DeFi. We propose a deep learning model, anomaly VAE-Transformer, which combines the variational autoencoder to extract local information in the short term, and the transformer, to identify dependencies between data in the long term. Based on a deep understanding of DeFi protocols, the proposed model collects and analyzes various on-chain data of Olympus DAO, a representative DeFi protocol, for extracting features suitable for anomaly detection. Then, we demonstrate the superiority of the proposed model by analyzing four anomaly cases detected successfully by the proposed model in Olympus DAO. A malicious attack attempt and structural changes in DeFi protocols can be identified quickly using the proposed method; this is expected to help protect the assets of DeFi users and improve the safety, reliability, and transparency of the DeFi market. The dataset and codes are available at
https://github.com/fialle/Anomaly-VAE-Transformer