Graduate research

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4

Discussion

Several sources indicate a surge in the incidence and losses from bitcoin fraud, causing cryptocurrency scams to become a global problem. The necessity to examine prevalent problems and Ponzi schemes connected to cryptocurrencies and how Studies might mitigate the instances has arisen due to the surge in bitcoin fraud. Like He et al., Galletta & Pinelli (2023) utilized open source (2022). Trozze et al. (2022) used published databases, whereas Phan et al. (2019) used social media (Twitter). According to every study, there has been an increase in bitcoin fraud. Even though all research conclusions indicated increased cryptocurrency fraud, their target areas varied. Different studies present proposals for dealing with fraud in crypto. Some authors have identified deep learning techniques to help deal with feature engineering concerns in this topic. Other researchers propose that a detection platform for Ponzi contracts may be suitable to reduce the chances of conning investors. Future research will also require the use of a greater source of data compared to the current one used by Phan and Mentzer (2019).

Evidence-Based Recommendation

Galletta and Pinelli, (2023) argued that researchers should expand the solution model in many ways in further work. The researchers should also consider the bytecode of contracts available in the dataset but not utilized by his classifier and employ deep learning techniques to decrease the feature engineering work. Future research should enhance the process for optimizing the optimal collection of features. Phishing is one of the most promising frauds on Ethereum that may be identified similarly using this method (Phan, Li & Mentzer, 2019). The researcher must study the process of discovering Ethereum Ponzi contracts more thoroughly. Future research should use a deep learning methodology to extract serialized characteristics from contracts' bytecodes, then train them. Studies can discover Ponzi contracts by examining the similarity of bytecode sequences amongst contracts. Academics should create a platform for identifying and tracking Ethereum Ponzi contracts to safeguard investors from fraud. Ponzi contracts on Ethereum require ongoing study to keep the system secure and stable.

The knowledge of how fraudulent actions are carried out and how to spot and stop them has to be expanded via more study. The frequency of these terms may be studied over a longer period using time-series analysis. Research should reveal some trends when comparing how individuals talked about scams and other fraudulent actions to when these activities really occurred (Phan, Li & Mentzer, 2019). It's also vital to remember that fraud detection and prevention are supported by blockchain technology. Unfortunately, there are some fraudulent behaviors that the technology also encounters due to weaknesses or scams. It may be erroneous to infer whether words are good or negative when we analyze them and base our analysis on their rating value.

References

Galletta, L., & Pinelli, F. (2023). Sharpening Ponzi Schemes Detection on Ethereum with Machine Learning.  arXiv preprint arXiv:2301.04872. https://arxiv.org/pdf/2301.04872.pdf

He, X., Yang, T., & Chen, L. (2022). CTRF: Ethereum-Based Ponzi Contract Identification.  Security and Communication Networks,  2022. https://doi.org/10.1155/2022/1554752

Phan, L., Li, S., & Mentzer, K. (2019). Blockchain technology and the current discussion on fraud. Information Systems And Analytics Journal Articles. Paper 28. https://digitalcommons.bryant.edu/cgi/viewcontent.cgi?article=1027&context=cisjou\

Trozze, A., Kamps, J., Akartuna, E. A., Hetzel, F. J., Kleinberg, B., Davies, T., & Johnson, S. D. (2022). Cryptocurrencies and future financial crime.  Crime Science,  11, 1-35. https://doi.org/10.1186/s40163-021-00163-8