Analyzing Various Machine Learning Algorithms for Blockchain-Based Fraud Detection (International Journal of Innovative Research in Computer Science & Technology, 2022)
A blockchain network's economics and  user confidence can be seriously harmed by fraud. Consensus algorithms like proof of work and proof of  stake can verify the legitimacy of a transaction but not the  identity of the people who are conducting or verifying it. On a blockchain network, fraud can still occur, as a result  of this. One approach to fighting fraud is to make use of  machine learning techniques. There are two types of  machine learning: supervised and unsupervised. We use a  variety of supervised machine learning techniques in this  study to distinguish between legitimate and fraudulent  purchases. We also compare decision trees, Naive Bayes,  logistic regression, multilayer perceptron, and other  supervised machine learning techniques in detail for this  challenge.
Keywords: Blockchain; Machine; Learning; Machine Learning Algorithms; Fraud Detection