Anomaly detection in blockchain using network representation and machine learning

Abstract

The vast majority of digital currency transactions rely on a blockchain framework to ensure quick and accurate execution. As such, understanding how a blockchain works is vital to understanding the dynamics of cryptocurrency operations. One of the key benefits of this type of system is the exhaustive records captured in a given marketplace. The interwoven movement between agents can effectively be expressed as a graph via the extraction of historical data from the blockchain. By looking at a specific blockchain as an interaction of its agents, network representation learning can be leveraged to examine these relationships. Furthermore, the analysis of a graph structure can be enhanced through the application of modern and sophisticated machine learning techniques. Leveraging the automated nature of these methods can create meaningful observations of the input network. In this paper, we utilize several machine learning models to detect anomalous transactions in various digital currency markets. We find that supervised learning techniques yield encouraging results, whereas unsupervised learning techniques struggle more with the classification.

A survey of deep learning applications in cryptocurrency

“… Martin et al. 176 GNN, GAT Transaction data on Ethereum blockchain from August 2, 2016 to January 15, 2017 Graph neural network algorithm has higher anomaly detection accuracy. An anomaly detection framework for Ethereum blockchain based on OCGNN is proposed.…”

“…Regarding abnormal trading detections, researchers first employed K-means clustering and SVM models. 174 , 175 , 176 For example, Monamo et al. 174 used the Trimmed K-means algorithm to detect unsupervised cybercrimes, successfully detecting some known fraudulent activities and improving the detection rate for known fraudulent elements.…”

Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis

“…Additionally, the extent of Bitcoin's anonymity is investigated by clustering, identifying, and categorizing Bitcoin addresses, which sheds light on the possibility of revealing the identity of users or organizations in the Bitcoin ecosystem. Reference [66] suggests utilizing multiple ML models for the identification of anomalous transactions in different digital currency markets. On the other hand, [67] aimed to develop a deep learning model based on a multiplicative long short-term memory (LSTM) architecture and an attention mechanism that incorporates technical indicators to enhance the accuracy and reduce the error rate of bitcoin price prediction.…”

Feature Engineering for Anomaly Detection and Classification of Blockchain Transactions

“…Martin et al, [17] proposed a conceptual analysis of various digital currency markets to detect anomalous Bitcoin entities. Their experimental data focused on the user activity or interaction when purchasing and selling digital coins on a free and open market platform.…”