Predicting Cryptocurrency Fraud Using ChaosNet: The Ethereum Manifestation

Dutta, Anurag; Voumik, Liton Chandra; Ramamoorthy, Athilingam; Ray, Samrat; Raihan, Asif

doi:10.3390/jrfm16040216

Cited by 18 publications

(4 citation statements)

References 38 publications

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“…This made it more challenging for the company to compete in fast-changing edtech. The company overstaffed several sectors and overlooked vital skill shortages (Dutta et al 2023).…”

Section: Misallocation Of Resourcesmentioning

confidence: 99%

A case study of BYJU’S failure

Samrat Ray,

Kaviya Jain,

Piyush Birru

et al. 2024

World J. Adv. Res. Rev.

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Once an education IT pioneer, BYJU'S, failure convoluted startup workers, investors. Byju failed due to financial mismanagement. Bad decisions and inconsistent financing prevented the corporation from paying its obligations. Company disagreements and leadership instability hurt BYJU'S operations. Due to negative press, BYJU'S brand suffers, its reputation suffered from data breaches, false advertising, and forced purchases. When the economy is terrible, earning more is tougher. These issues must be addressed for the future of technology. Businesses must be frugal, transparent, and financially aware. Customise marketing and market segmentation to accommodate everyone. Openness to new ideas and flexibility are needed to compete in a fast-changing market. Innovation, perseverance, and morality help Ed-tech overcome problems and capture opportunities. Businesses may learn from BYJU'S errors to grow sustainably, establish trust, and lead in the fast-paced, competitive educational technology business. Ed-tech must innovate after Byju failed. The company couldn't innovate; therefore, it struggled to compete and survive. Edtech companies must be inventive, customer-focused, and adaptable to compete in a competitive, ever-changing business. Learning from setbacks and encouraging resilience and creativity may help ed-tech companies thrive.

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“…This made it more challenging for the company to compete in fast-changing edtech. The company overstaffed several sectors and overlooked vital skill shortages (Dutta et al 2023).…”

Section: Misallocation Of Resourcesmentioning

confidence: 99%

A case study of BYJU’S failure

Samrat Ray,

Kaviya Jain,

Piyush Birru

et al. 2024

World J. Adv. Res. Rev.

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“…During the "dot-com bubble", a surge of information technology startups produced an infusion of revenue for Silicon Valley Bankers, known for its propensity (Dutta et al 2023a) to finance managers and unlucrative shareholders. In 1995, amongst 2000 clients were telecommunications pioneers Cisco Systems (Chen 2017) and Bay Networks (Bay Networks, Inc. 1998).…”

Section: Remembrance-a Dive Into the Pastmentioning

confidence: 99%

The Silicon Valley Bank Failure: Application of Benford’s Law to Spot Abnormalities and Risks

Dutta,

Voumik,

Kumarasankaralingam

et al. 2023

Risks

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Data are produced every single instant in the modern era of technological breakthroughs we live in today and is correctly termed as the lifeblood of today’s world; whether it is Google or Meta, everyone depends on data to survive. But, with the immense surge in technological boom comes several backlashes that tend to pull it down; one similar instance is the data morphing or modification of the data unethically. In many jurisdictions, the phenomenon of data morphing is considered a severe offense, subject to lifelong imprisonment. There are several cases where data are altered to encrypt reliable details. Recently, in March 2023, Silicon Valley Bank collapsed following unrest prompted by increasing rates. Silicon Valley Bank ran out of money as entrepreneurial investors pulled investments to maintain their businesses afloat in a frigid backdrop for IPOs and individual financing. The bank’s collapse was the biggest since the financial meltdown of 2008 and the second-largest commercial catastrophe in American history. By confirming the “Silicon Valley Bank” stock price data, we will delve further into the actual condition of whether there has been any data morphing in the data put forward by the Silicon Valley Bank. To accomplish the very same, we applied a very well-known statistical paradigm, Benford’s Law and have cross-validated the results using comparable statistics, like Zipf’s Law, to corroborate the findings. Benford’s Law has several temporal proximities, known as conformal ranges, which provide a closer examination of the extent of data morphing that has occurred in the data presented by the various organizations. In this research for validating the stock price data, we have considered the opening, closing, and highest prices of stocks for a time frame of 36 years, between 1987 and 2023. Though it is worth mentioning that the data used for this research are coarse-grained, still since the validation is subjected to a larger time horizon of 36 years; Benford’s Law and the similar statistics used in this article can point out any irregularities, which can result in some insight into the situation and into whether there has been any data morphing in the Stock Price data presented by SVB or not. This research has clearly shown that the stock price variations of the SVB diverge much from the permissible ranges, which can give a conclusive direction on further investigations in this issue by the responsible authorities. In addition, readers of this article must note that the conclusion formed about the topic discussed in this article is objective and entirely based on statistical analysis and factual figures presented by the Silicon Valley Bank Group.

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“…By adjusting the hyper-parameters of the LGBM further, an accuracy of 99.03% is attained. Dutta et al [4] aim to find evidence of fraud and deceit in Ethereum transaction procedures. Using generalized Luroth series maps, an artificial neural network called ChaosNet enables us to achieve this functionality.…”

Section: Introductionmentioning

confidence: 99%

Cryptocurrency fraud detection through classification techniques

Tripathy,

Kumar Balabantaray,

Parida

et al. 2024

IJECE

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Ethereum and its native cryptocurrency, Ether, have played a worthy attention in the development of the blockchain and cryptocurrency space. Its programmability and smart contract capabilities have made it a foundational platform for decentralized applications and innovations across various industries. Because of its anonymous and decentralized structure, the hotheaded expansion of cryptocurrencies in the payment space has created both enormous potential and concerns related to cybercrime, including money laundering, financing terrorism, illegal and dangerous services. As more financial institutions attempt to integrate cryptocurrencies into their networks, there is an increasing need to create a more transparent network that can withstand these kinds of attacks. In this work, we are using different classification techniques, such as logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) for Ethereum fraud detection. The dataset we are using includes rows of legitimate transactions done using the cryptocurrency Ethereum as well as known fraudulent transactions. The “XGBoost” model, which is noteworthy, detects variations that might attract notice and prevent potential issues in this chore.

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Predicting Cryptocurrency Fraud Using ChaosNet: The Ethereum Manifestation

Cited by 18 publications

References 38 publications

A case study of BYJU’S failure

A case study of BYJU’S failure

The Silicon Valley Bank Failure: Application of Benford’s Law to Spot Abnormalities and Risks

Cryptocurrency fraud detection through classification techniques

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