Blockchain Analytics Based on Artificial Intelligence: Using Machine Learning for Improved Transaction Analysis

Blockchain technology has reshaped numerous industries by providing secure and transparent transactional platforms. This paper delves into the intersection of blockchain analytics and artificial intelligence (AI) to advance transaction analysis. The primary aim is to bolster fraud detection and enhance transaction efficiency. Through a comprehensive literature review, we identify gaps in existing knowledge and lay the groundwork for our research. We introduce a novel transaction-hybrid model developed using machine learning (ML) algorithms, including support vector machines (SVMs), K-nearest neighbors (KNNs), and random forest (RF). This transact-hybrid model aims to fortify fraud detection capabilities by harnessing the strengths of each algorithm. We curate a unique dataset comprising 1000 instances, incorporating critical transaction features such as transaction hash, block number, transaction fee and gas limit, with binary classification indicating fraudulent transactions. Meticulous preprocessing, including feature engineering and data splitting for training and testing, is conducted. Visualization techniques, including seaborn-based graphs, correlation plots and violin plots, elucidate the dataset’s characteristics. Additionally, a spring colormap correlation map enhances the understanding of feature relationships. Transaction fee distributions before and after preprocessing are visually presented, highlighting the impact of data preparation. We introduce the novel transact-hybrid classifier (THC) with detailed mathematical equations, emphasising its contribution to transactional fraud detection. The classifier integrates SVM, KNN and RF outputs using an exclusive OR operation, showcasing innovation in model development. To evaluate model performance, we conduct a comparative analysis, incorporating SVM, KNN, RF and a voting classifier. Bar plots for accuracy, precision, recall and F1 score, with a custom plasma colormap, offer a visual summary of each model’s metrics. Furthermore, a receiver operating characteristics (ROC) curve analysis is presented, highlighting the area under the curve (AUC) for SVM, KNN, RF and voting models, providing a comprehensive view of their performance in distinguishing between true positive and false positive rates. Our proposed method demonstrates over 99% efficacy in fraud detection, underscoring its potential impact in transaction analysis.