Complete Draft of the Literature Review Section
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Phase 13a: Ponzi Schemes in relation to Cryptocurrency
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Phase 13a: Ponzi Schemes in relation to Cryptocurrency
W(5)H(1): New Research Article #1: Cryptocurrencies and future financial crime
1. How was data collected (methodology)? Cut and paste the paragraph below that describes the methodology and HIGHLIGHT the indicator words that specifically show you the methodology :
Data was collected through systematic review of various literature and research on cryptocurrency scams.
“This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) protocol.”
“To be considered for this scoping review, published studies had to meet various eligibility criteria. First, we limited our review to publications written in English as we relied entirely on our reviewers’ language skills. The academic literature portion of the scoping review exclusively focused on academic articles such as peer-reviewed journals and conference papers due to the study’s aim of mapping out current research activities. The grey literature review included reports, publications, and alerts. By implication, the review excludes publications such as blog posts, op-eds, presentations, newsletters, marketing materials, correspondence, and magazine or newspaper articles” (Trozze et al., 2022).
“The review used Google Scholar (GS) to identify academic studies for review and Google’s Search Engine to identify private and public sector publications potentially eligible for review. 2 One of the authors (AT) performed the final and most recent search on GS and Google’s Search Engine in November 2020” (Trozze et al., 2022).
What were the findings? Cut and paste the paragraph below that describes the findings and HIGHLIGHT the sentences that specifically show you the summary of findings:
The results indicated that there are 29 types of fraud associated with cryptocurrency.
“Academic publications most frequently referred to Ponzi schemes and (synonymous) high yield investment programmes (HYIPs). These scam types were discussed in 44.4% of the included studies. Eighteen (28.6%) publications analysed scams involving initial coin offerings (ICOs). Ten analyses (15.9%) covered phishing scams and nine (14.3%) discussed unspecified types of fraud. Seven (11.1%) studies covered pump-and-dump schemes and market manipulation. Six (9.5%) studies looked at exchange scams and five (7.9%) at scam wallet services. Four papers (4.8%) discussed each of the following types of fraud: fraudulent cryptocoins, smart contract honeypots / attacks, and mining scams. Three publications (4.8%) discussed mining malware and the same number addressed smart Ponzi schemes. Two (3.2%) publications discussed securities fraud and identity theft. Sixteen fraud categories were only mentioned in a single (1.6%) publication each. The second iteration of the search identified 17 new types of fraud from the literature” (Trozze et al., 2022).
“Altogether, 36 of the grey literature publications came from private sector companies. These publications identified 32 different types of cryptocurrency fraud, 14 of which were not identified in the academic literature” (Trozze et al., 2022).
“Most private sector studies (63.9%) referred to some unspecified type of fraud or scam. Fourteen (38.9%) publications analysed scams involving ICOs and 13 (36.1%) discussed Ponzi schemes or HYIPs. Nine (25.0%) studies covered phishing and seven (19.4%) covered mining malware. Four studies (11.1%) looked at SIM swapping, which did not appear in the academic literature, and which is defined in Appendix 3: Table Table7.7. Four studies (11%) also discussed giveaway scams. Three studies (8.3%) discussed market manipulation, forex fraud, and/or exchange scams. Two studies (5.6%) looked at impersonation scams, mining scams, pump-and-dumps, and/or securities fraud. Eighteen fraud categories were mentioned in a single publication each (2.8%)” (Trozze et al., 2022).
W(5)H(1): New Research Article #2: Sharpening Ponzi Schemes Detection on Ethereum with Machine Learning
1. How was data collected (methodology)? Cut and paste the paragraph below that describes the methodology and HIGHLIGHT the indicator words that specifically show you the methodology :
The methodology entails application of experimental procedures.
“In this section, we build binary classifiers for detecting smart Ponzi contracts, and we perform an experimental evaluation to study how the new features of Section IV impact classification and the quality of the obtained classifiers.”
2. What were the findings? Cut and paste the paragraph below that describes the findings and HIGHLIGHT the sentences that specifically show you the summary of findings:
The results showed that it is possible to assess and effectively classify cryptocurrency fraud from a credible investment.
“The two populations present a different behaviour for what concerns the input transactions (Tx_in): the plot of the cumulative distribution presents a very different shape. Typically, smart Ponzi presents a small number of input transactions with few exceptions with many transactions. The same happens for features Investment_in/TX_in, Payment_out/TX_out where smart Ponzi generally present larger values than the other class. We expect that these features provide the classifier with a relevant contribution to discriminate between the two classes” (Galletta & Pinelli, 2023).
W(5)H(1): New Research Article #3: Blockchain technology and the current discussion on fraud.
1. How was data collected (methodology)? Cut and paste the paragraph below that describes the methodology and HIGHLIGHT the indicator words that specifically show you the methodology :
The data was collected through observation.
“To understand how fraud and blockchain topic are discussed on social media, a twitter listener was developed using Apache Flume and approximately 2 million tweets were collected during November 8th and December 31, 2018 using “blockchain” as the keyword. From this pool of tweets, 7,901 are tweets that include both “ fraud” and “ blockchain” in the tweet text and are written in English. Those tweets will be used in the analysis. About 41% (3,199) are original tweets, 51% of them are retweets (4,062) and the rest (8%, 649 tweets) are either quoted tweets or replies. Issues in Information Systems Volume 20, Issue 4, pp. x-x, 2019 13 This following section will first discuss top words and sentiment words in original tweets on blockchain fraud discussion, followed by a sentiment analysis about this topic. We also identify top influencers in the topic by looking at the frequency of retweets and eigenvector centrality” (Phan et al., 2019).
2. What were the findings? Cut and paste the paragraph below that describes the findings and HIGHLIGHT the sentences that specifically show you the summary of findings:
The findings indicate that fraud in cryptocurrency has become commonplace and has influenced conversations.
“The findings of this research show that the most frequently mentioned words in tweets include scam, tax, combat, fight, bitcoin, crypto, payment, Asia, Japan, Germany, Thailand and banks. The top sentiment words are scam, combat, fight, fraudulent, prison, swift, frauds, hacked, prevent, and guilty” (Phan et al., 2019).
“It can be seen that people are more aware and concerned about the scams and barely mentioned other security attacks such as the 51% attack and hard fork. In addition, the frequency and severity of scams are much greater than other issues because blockchain is structured in a very secure way, making other attacks more difficult to occur” (Phan et al., 2019).
W(5)H(1): New Research Article #4: CTRF: Ethereum-Based Ponzi Contract Identification
1. How was data collected (methodology)? Cut and paste the paragraph below that describes the methodology and HIGHLIGHT the indicator words that specifically show you the methodology :
Data was collected through conducting an experiment.
“In our experiments, we eliminated two non-Ponzi contract addresses that were not successfullydeployed and found two addresses with exactly opposite labels to other address sets” (He et all., 2022).
“ Datasets. To compare the validity of the datasets and features, we did experiments on four main datasets” (He et all., 2022).
“We conducted independent experiments on these four datasets: first cross-validating to find the best experimental parameters, then using 70% of the dataset for training and 30% for testing, and finally conducting 20 experiments to calculate the average results” (He et all., 2022).
2. What were the findings? Cut and paste the paragraph below that describes the findings and HIGHLIGHT the sentences that specifically show you the summary of findings:
Ponzi schemes are evident in matters to do with cryptocurrency.
“We observed a large number of Ponzi contracts and found that when most of them receive a transfer from an external account, they will determine whether the amount of the transfer is less than the minimum investment threshold set by the contract” (He et all., 2022).
Parameters and Database used
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. 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. 10.1186/s40163-021-00163-8