Machine Learning Techniques for Detecting Abnormal Behaviors in Blockchain Technologies: A Methodological Review

Abstract

A blockchain is made up of an ordered list of nodes connected by links known as chains. The nodes in the blockchain store data and are stored together. The distributed and decentralized ledger technology, blockchain, empowers cryptocurrencies like Bitcoin and Ethereum. It makes a safe and open record of transactions by permitting the distribution of digital data as a “block” but prohibiting its duplication. Furthermore, blockchain-based anomaly detection tools, which always automatically detect and weed out abnormal behaviors, are essential for protecting networks and systems from unforeseen intrusions. A smart contract could monitor real-time transaction volumes, access patterns, or resource usage. If anomalies are detected, such as unusual spikes in activity, the smart contract can trigger alerts or take predefined actions. Numerous anomaly detection analysis techniques have been put out and used in the scientific literature in various fields. This paper provides an overview of the latest machine learning techniques for identifying abnormal behaviors in blockchain, such as supervised, unsupervised, and deep learning. We also go over a few of the applications for anomaly behaviors detection.