Detecting malicious blockchain transactions using graph neural networks

ST Jeyakumar, AC Eugene Yugarajah, Z Hóu… - … on Distributed Ledger …, 2023 - Springer
The adoption of blockchain technology within various critical infrastructures is on the rise.
Concurrently, there has been a corresponding increase in its misuse, primarily through the …

Competence of graph convolutional networks for anti-money laundering in bitcoin blockchain

I Alarab, S Prakoonwit, MI Nacer - Proceedings of the 2020 5th …, 2020 - dl.acm.org
Graph networks are extensively used as an essential framework to analyse the
interconnections between transactions and capture illicit behaviour in Bitcoin blockchain …

SigTran: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks

F Poursafaei, R Rabbany, Z Zilic - Pacific-Asia Conference on Knowledge …, 2021 - Springer
Cryptocurrency networks have evolved into multi-billion-dollar havens for a variety of
disputable financial activities, including phishing, ponzi schemes, money-laundering, and …

Detecting malicious Ethereum entities via application of machine learning classification

F Poursafaei, GB Hamad, Z Zilic - 2020 2nd conference on …, 2020 - ieeexplore.ieee.org
Malicious activities such as scams and frauds have imposed high costs for financial systems.
The advent of blockchain-based cryptocurrencies such as Ethereum provides …

Comparative Study Analysis of MachineLearning Algorithms for Anomaly Detection in Blockchain

R Saravanan, S Santhiya, K Shalini… - 2023 International …, 2023 - ieeexplore.ieee.org
Anomaly detection is one of the challenging problems encountered by the modern network
security industry. In these last years, Blockchain technologies have been widely used in …

Anomaly detection in blockchain using network representation and machine learning

K Martin, M Rahouti, M Ayyash… - Security and Privacy, 2022 - Wiley Online Library
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 …

FraudLens: Graph Structural Learning for Bitcoin Illicit Activity Identification

J Nicholls, A Kuppa, NA Le-Khac - … of the 39th Annual Computer Security …, 2023 - dl.acm.org
Illicit activity in cryptocurrency has increased dramatically over the years. Bitcoin mechanics
allow for users to mask their identity through obfuscation techniques. Much research has …

A Graph-Based Visual Modeling for Enhancing Suspicious Node Detection in Blockchain Networks

S Jeyakumar, EYA Charles, Z Hou… - Available at SSRN … - papers.ssrn.com
Blockchain technology has gained significant attention due to its transparent and
decentralized nature. The inherent pseudonymity in blockchain transactions, however …

On-graph Machine Learning-based Fraud Detection in Ethereum Cryptocurrency Transactions

H Milner, R Mahmud, M Afrin… - 2023 IEEE 22nd …, 2023 - ieeexplore.ieee.org
The popularity of Ethereum as a platform for Stablecoin transactions (for example, AUDN)
continues to rise. It is therefore paramount that the integrity and security of transactions …

A labeled transactions-based dataset on the ethereum network

S Al-E'mari, M Anbar, Y Sanjalawe… - … Conference on Advances …, 2020 - Springer
A few datasets of blockchain networks are available to be used in evaluating intrusion
detection systems, and some of the proposed detection systems are evaluated as self …