| Summary: | Cryptocurrencies have been a target for cybercriminal activities because of the pseudo-anonymity and privacy they offer. Researchers have been actively working on analyzing and developing innovative defensive mechanisms to prevent these activities. A significant challenge facing researchers is collecting datasets to train defensive systems to detect and analyze these cyberattacks. Our aims in this systematic review are to explore and aggregate the state of the art threats that have emerged with cryptocurrencies and the defensive mechanisms that have been proposed. We also discuss the threats type, scale, and how efficient the defensive mechanisms are in providing early detection and prevention. We also list out the resources that have been used to collect datasets, and we identify the publicly available ones. In this study, we extracted 1,221 articles from four top scientific and engineering databases and libraries in Computer Science: IEEE Xplore, ACM Digital Library, Elsevier's Scopus, and Crarivate's Web of Science. We defined inclusion, exclusion, and quality of assessment criteria, and after a detailed review process, 66 publications were included in the final review. Our analysis revealed that the literature contains a significant amount of research to detect and analyze several attack types, such as the high yield investment programs and pump and dump. These attacks have been used to steal millions of USD, abuse millions of connected devices, and have created even more significant loss in denial of services and productivity losses. We have found that the researchers use various sources to collect training datasets. Many authors have made their dataset publicly available. We have created a list of these datasets, which we have made available along with other supplementary websites, tools, and libraries that can be used in the data collection and analysis process.
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