Anomaly Detection in Blockchain Network Using Unsupervised Learning

Abstract

Blockchain technology is gaining popularity and is widely used in cryptocurrencies, NFTs, and the financial sector. With the increasing number of transactions and the expansion of blockchain networks, a higher risk of fraudulent activities occurs, which is difficult to monitor and detect manually. Therefore, anomaly detection in blockchain data becomes an important approach for identifying suspicious activities, system errors, or other unusual behaviors. This article deals with the application of data analytics methods to the detection of anomalies in transaction data of the WAX Blockchain network, with a focus on NFT sales. In our research, we implemented a method for tracking NFT sales, focusing on identifying significant price deviations as potential indicators of suspicious activity. This method assumes that while the market value of NFTs can fluctuate, a transaction with a significantly higher price than usual may signal potential money laundering, market manipulation, or other illicit activities.