One of the most popular platforms in token-based banking is the flexible Stellar platform. The wide range of Stellar’s features allows companies to use it in modern cryptocurrency and token-based banking. This network charges a fee for each transaction. A percentage of the net amount is generated as the inflation rate of the network due to the increased number of tokens. These fees and inflationary amounts are aggregated into a general account and ultimately distributed among the network members on a collective-vote basis. In this mechanism, network users select an account as the destination to which they wish to transfer assets using their user interface, generally a wallet. This account could be the account of charities that need this help. The target distribution network is then determined based on the voting results of all members. One of the challenges in this network is the targeted and fair distribution of these funds between accounts. In this paper, the first step is a complete infrastructure of a Stellar financial network that will consist of three network-based segments of the core network, an off-chain server, and a wallet interface. In the second step, a context-aware recommendation system is implemented to solve the targeted management of payroll account selection. The results of this study concerning the importance of the targeted division of collective assets show a context-aware recommendation system as a solution to improve the process of Stellar users’ participation in the voting process.

The popularity and remarkable attractiveness of cryptocurrencies, especially Bitcoin, absorb countless enthusiasts every day. 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 use the Blockchain technology of symmetry and asymmetry in computer and engineering science to present a new method for detecting anomalies in Bitcoin with more appropriate efficiency. In this study, a collective anomaly approach was used. Instead of detecting the anomaly of individual addresses and wallets, the anomaly of users was examined. In addition to using the collective anomaly detection method, the trimmed_Kmeans algorithm was used for clustering. The results of this study show the anomalies are more visible among users who had multiple wallets. The proposed method revealed 14 users who had committed fraud, including 26 addresses in 9 cases, whereas previous works detected a maximum of 7 addresses in 5 cases of fraud. The suggested approach, in addition to reducing the processing overhead for extracting features, detect more abnormal users and anomaly behavior.

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