Cryptocurrency Fraud Detection: A systematic Literature review

Abstract

Cryptocurrency Fraud is growing at alarming rate and spreading rapidly despite on-going mitigating efforts. This brings a necessity to find more effective solutions to detect these Frauds and prevent users from losing digital assets. This study uses the PRISMA statement as a reference so to be transparent. This paper uses a SLR to identify where recent studies in cryptocurrency fraud detection have been focused on and offers a broad perspective relating to types of Techniques, Algorithms, dataset sources used and also the categories of Fraud types in the research area within the range of 2018 to 2022. A total of 38 selected papers met the inclusion criteria based on title of articles, exclusion criteria, reading abstract and content of the selected 38 papers. Different data are extracted from these articles and recorded in an excel sheet for further analysis. Most of the paper discussed about the use of Machine Learning and Deep Learning analysis approach to analyse cryptocurrency fraud. We also identified research gaps that are further needed to be explored by the research community.