Machine Learning-Driven Detection and Prevention of Cryptocurrency Fraud
Anshika Sharma, Himanshi Babbar
Abstract
Numerous cutting-edge commercial possibilities have emerged as a result of the widespread use of cryptocurrencies, but it has also drawn a growing number of fraudulent individuals looking to commit fraud. This paper proposes an extensive strategy that makes use of machine learning(ML) techniques to meet the urgent demand for effective fraud detection tools inside the cryptocurrency industry. This paper presents a comprehensive investigation of several fraudulent practices that are common in the virtual currencies ecosystem. The next step has been to investigate several of ML approaches, including Adaptive Boosting(AdaBoost), Random Forest(RF) and Extreme Gradient Boosting(XGBoost), that are designed to spot unusual patterns suggestive of fraudulent behaviour. A crypto fraud detection dataset of fraud instances and real-world cryptocurrency transactions has been utilised in trials to gauge the effectiveness of the suggested approach. To gauge the accuracy and resilience of the models, performance metrics including precision, recall, and F1-score are used. In order to establish which algorithms are most suited for real-time fraud detection, multiple approaches have also been examined for efficiency and scalability. The results show how ML techniques can be used to improve the security of cryptocurrency networks. The XGBoost approach has the best accuracy, at 98%, followed by AdaBoost and RF, at 67% and 90% respectively. The suggested models show encouraging results in spotting fraudulent behaviour, with substantial successes in spotting previously unidentified attack patterns.