  The popularity and amazing attractiveness of cryptocurrencies, and especially
Bitcoin, absorb countless enthusiasts daily. Although Blockchain technology
prevents fraudulent behavior, it cannot detect fraud on its own. There are
always unimaginable ways to commit fraud, and the need to use anomaly detection
methods to identify abnormal and fraudulent behaviors has become a necessity.
The main purpose of this study is to present a new method for detecting
anomalies in Bitcoin with more appropriate efficiency. For this purpose, in
this study, the diagnosis of the collective anomaly was used, and instead of
diagnosing the anomaly of individual addresses and wallets, the anomaly of
users was examined, and the anomaly was more visible among users who had
multiple wallets. In addition to using the collective anomaly detection method
in this study, the Trimmed_Kmeans algorithm was used for clustering and the
proposed method succeeded in identifying 14 users who had committed theft,
fraud, and hack with 26 addresses in 9 cases. Compared to previous works, which
detected a maximum of 7 addresses in 5 cases of fraud, the proposed method has
performed well. Therefore, the proposed method, by presenting a new approach,
in addition to reducing the processing power to extract features, succeeded in
detecting abnormal users and also was able to find more transactions and
addresses committed a scam.
