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Blockchain has facilitated the growth of cryptocurrencies but has also provided new ideas for illegals to commit fraud. Research on malicious accounts detection shows that the number of malicious accounts is much smaller than that of benign accounts, leading to imbalanced dataset samples. Most researchers adopt the under-sampling method to help deal with this issue, but this method does not correspond to the actual scale. So, we propose an anomaly detection method based on community discovery. Firstly, we use the transaction information in the Ethereum public chain to build a transaction network and use the Louvain algorithm to divide the transaction network into communities. Secondly, we use the LightGBM algorithm to classify the community. Finally, based on the classification results, we use HBOS, LOF, K-Means, KNN and iForest algorithms as benchmark algorithms for anomaly detection and …