Machine Learning for Fraud Detection in Blockchain Transaction

Fraud detection in blockchain transactions is critical as the technology becomes more integrated into financial systems. Traditional rule-based systems have become ineffective against new fraudulent tactics. To solve these problems, a unique machine learning-based approach for detecting fraud in blockchain transactions is proposed in the study. Advanced machine learning techniques such as Random Forests, Support Vector Machines, and Isolation Forest are used to overcome existing systems’ limitations. The data are collected meticulously and preprocessed, feature engineering is employed, and the models are trained to improve accuracy and adaptability. Compared to existing systems, the proposed technique outperforms with an accuracy of 0.98, precision of 0.94, recall of 0.93, and a false positive rate of 0.06. It also has an average processing time of 70 milliseconds, improving computational efficiency. The study represents a significant step forward in blockchain transaction security, providing dynamic protection against fraudulent activity and bolstering the integrity of decentralized financial ecosystems.