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Machine Learning Algorithms Expose Ponzi Scheme on Ethereum Network

Algorithms, Ethereum, Expose, Learning, Machine, Network, Ponzi, Scheme

“Unraveling the tangled web of Ponzi schemes on Ethereum: Harnessing the power of machine learning algorithms to safeguard investors and preserve the integrity of decentralized finance.”



Ponzi schemes have been a topic of interest and concern for many years, and researchers from various fields have conducted studies to gain a deeper understanding of these deceptive practices. In a recent article, Chiluwa, Kamalu, and Anurudu (2022) conducted a critical discourse analysis of the MMM Nigeria Ponzi scheme, shedding light on the deceptive transparency and masked discourses used by the scheme’s operators. Their study provides valuable insights into the strategies employed by Ponzi schemes to manipulate and deceive participants.

In another study, Jory and Perry (2011) critically analyzed Ponzi schemes, highlighting their inherent flaws and the devastating consequences they can have on individuals and society. The authors emphasize the need for increased awareness and regulation to prevent such schemes from causing financial harm.

The impact of Ponzi schemes is not limited to individuals; it can also have significant effects on a country’s economy. Mohammed (2021) examined the case of Ghana and explored the economic consequences of Ponzi schemes. The study highlights the need for effective regulatory measures to protect the financial well-being of individuals and the stability of the economy.

Detecting and preventing Ponzi schemes is a challenging task, especially with the rise of digital currencies and blockchain technology. Bartoletti, Pes, and Serusi (2018) discuss the use of data mining techniques to detect Bitcoin Ponzi schemes, highlighting the potential of technology in identifying and combating fraudulent activities.

While technology can play a crucial role in uncovering Ponzi schemes, it also poses security challenges. Moore (2013) discusses the promise and perils of digital currencies, emphasizing the importance of information security and the need for robust authentication methods, such as iris-scan authentication, to protect users’ assets.

Blockchain technology, with its decentralized and transparent nature, has been hailed as a potential solution to various problems, including Ponzi schemes. Li et al. (2019) propose an information security model based on blockchain and intrusion sensing in the IoT environment, highlighting the potential of this technology in enhancing security and preventing fraudulent activities.

In addition to technological solutions, effective text classification methods are also essential for detecting and preventing Ponzi schemes. Abiodun et al. (2021) conducted a systematic review of emerging feature selection optimization methods for optimal text classification, highlighting the potential of these techniques in enhancing the accuracy and efficiency of Ponzi scheme detection.

Regulatory responses to Ponzi schemes vary across different countries and regions. Tajti (2022) discusses the diverging regulatory approaches to Ponzi schemes in Hungary, while Tajti (2019) provides a comparative account of the regulatory responses in China, Europe, and the United States. These studies shed light on the challenges faced by regulators in addressing Ponzi schemes and the need for international cooperation in combating fraudulent activities.

Overall, the studies discussed here highlight the multidimensional nature of Ponzi schemes and the importance of interdisciplinary research and collaboration in understanding and combating these deceptive practices. From critical discourse analysis to technological innovations and regulatory measures, researchers and policymakers continue to explore various avenues to protect individuals from falling victim to Ponzi schemes and safeguard the stability of financial systems.

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