A New Framework for Fraud Detection in Bitcoin Transactions

Abstract

With the increased usage of Bitcoin and othercryptocurrencies, there is a need to address issues related tofraud detection in cryptocurrency systems. Such issuesinclude double-spending, money laundering, and accounthacking, among others, that Bitcoin needs to guard against.However, since Bitcoin is decentralised and transactions arenot reversible, the use of central-system approaches cannotbe applied; thus, an alternative approach must be adopted.The presented project offers a viable method of usingmachine learning for Bitcoin fraud detection. The frauddetection method is real-time, using ensemble stacking,which entails combining multiple machine learning modelsto enhance prediction capabilities. Algorithms to be usedinclude Random Forest, Gradient Boosting (XGBoost,LightGBM), Support Vector Machine (SVM), LogisticRegression, and Isolation Forest. In other words, multiplealgorithms will be used to examine Bitcoin transaction data,such as amounts transacted, transaction frequency, andtransaction patterns. Ensemble stacking allows the use of thestrengths of multiple algorithms, while the real-time functionenhances the applicability of the approach. Scalability isanother critical consideration, especially considering thenumber of Bitcoin users. This is why the use of a Flaskapplication server will be necessary for user datasubmissions, visualisation, and sending fraud notifications.Evaluation will be based on accuracy, precision, recall, andF1-score.Conclusion – The proposed solution appears quiteplausible as the fraud detection through machine learning isefficient, while scalability is one of the main features of theapproach.