Anomaly Detection in Blockchain Transactions: A Comparative Study of Isolation Forest, K-Means Clustering, and LSTM Models

Blockchain technology has gained widespread adoption across various industries due to its potential for secure and transparent data management. However, ensuring the integrity and security of transactions within blockchain networks is a critical challenge. Anomaly detection plays a pivotal role in identifying irregularities and potential fraudulent activities within these networks. This research paper delves into the intricacies of anomaly detection in blockchain data, employing a multifaceted approach that encompasses data preprocessing, machine learning models, and feature selection. The study employs the Isolation Forest and K-Means clustering models, offering robust solutions for identifying anomalies in blockchain transactions. Additionally, Long Short-Term Memory (LSTM) networks are leveraged for time series analysis. The results reveal insights into the strengths and limitations of these models, enhancing security measures and data integrity within the blockchain landscape. This comprehensive exploration aids in fortifying the trustworthiness of blockchain networks by detecting unusual and potentially fraudulent activities, contributing to the broader field of anomaly detection in digital transactions.