A comprehensive review of significant learning for anomalous transaction detection using a machine learning method in a decentralized blockchain network

Abstract

Blockchain technology has changed the global trading of assets. A blockchain can be viewed as a connected ledger managed by a distributed peer-to-peer (P2P) network. Blockchain offers distinctive characteristics such as transactional privacy, the immutability of data, transparency and cryptographic, among others. These features paved the door for blockchain to develop numerous technology solution, including voting applications [1,2], internet of things (IoT) [3,4], and supply chain management (SCM) [5,6], among others. The increasing desire for technological advancements stimulated the development of BT.

An interpretable ensemble model framework for real-time anomaly detection and prediction of Ethereum blockchain transactions

“…However, even a slight change in the data input will produce a different hash value [7]. The foundation of blockchain architecture is based on the concept of a P2P network [8]. Basically, this architecture allows sharing elements such as files, tasks, or work to be executed between several peers.…”