A Unified Approach to Fraudulent Detection

Dutta, Anurag; Choudhury, Manan Roy; De, Arnab Kumar

doi:10.37622/ijaer/17.2.2022.110-124

Cited by 11 publications

(2 citation statements)

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“…Only a limited portion of the network, known as its "peers," are accessible to each node. Every time a node wants to add a new transaction (Filliâtre and Paskevich 2013;Dutta et al 2022) to the chain, it sends copies of the transaction to all of its contemporaries, who then send copies to all of their contemporaries, and so forth. It spreads throughout the network in this way.…”

Section: Ethereummentioning

confidence: 99%

Predicting Cryptocurrency Fraud Using ChaosNet: The Ethereum Manifestation

Dutta

Voumik

Ramamoorthy

et al. 2023

JRFM

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Cryptocurrencies are in high demand now due to their volatile and untraceable nature. Bitcoin, Ethereum, and Dogecoin are just a few examples. This research seeks to identify deception and probable fraud in Ethereum transactional processes. We have developed this capability via ChaosNet, an Artificial Neural Network constructed using Generalized Luröth Series maps. Chaos has been objectively discovered in the brain at many spatiotemporal scales. Several synthetic neuronal simulations, including the Hindmarsh–Rose model, possess chaos, and individual brain neurons are known to display chaotic bursting phenomena. Although chaos is included in several Artificial Neural Networks (ANNs), for instance, in Recursively Generating Neural Networks, no ANNs exist for classical tasks entirely made up of chaoticity. ChaosNet uses the chaotic GLS neurons’ property of topological transitivity to perform classification problems on pools of data with cutting-edge performance, lowering the necessary training sample count. This synthetic neural network can perform categorization tasks by gathering a definite amount of training data. ChaosNet utilizes some of the best traits of networks composed of biological neurons, which derive from the strong chaotic activity of individual neurons, to solve complex classification tasks on par with or better than standard Artificial Neural Networks. It has been shown to require much fewer training samples. This ability of ChaosNet has been well exploited for the objective of our research. Further, in this article, ChaosNet has been integrated with several well-known ML algorithms to cater to the purposes of this study. The results obtained are better than the generic results.

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Section: Ethereummentioning

confidence: 99%

Predicting Cryptocurrency Fraud Using ChaosNet: The Ethereum Manifestation

Dutta

Voumik

Ramamoorthy

et al. 2023

JRFM

View full text Add to dashboard Cite

show abstract

“…Only a limited portion of the network, known as its "peers," are accessible to each node. Every time a node wants to add a new transaction [34] [35] to the chain, it sends copies of the transaction to all of its contemporaries, who then send copies to all of their contemporaries, and so forth. It spreads throughout the network in this way.…”

Section: Ethereummentioning

confidence: 99%

Predicting Ethereum Fraudulency using ChaosNet

Dutta¹,

Ray²

2023

Preprint

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Cryptocurrencies are in high demand right now, perhaps because of their volatile nature and untraceable difficulties. Bitcoin, Ethereum, Dogecoin, and others are just a few. This research seeks to identify falsehoods and probable fraudulences in Ethereum transactional processes. We have provided this capability to ChaosNet, an Artificial Neural Network constructed using Generalized Luroth Series maps. At many spatiotemporal scales, Chaos has been objectively discovered in the brain. Several synthetic neuronal simulations, including the Hindmarsh-Rose model, possess Chaos, and individual brain neurons are known to display chaotic bursting phenomenon. Although Chaos is included in several Artificial Neural Networks (ANNs), for instance, the Recursively Generating Neural Networks, no ANN exist for classical tasks that is fully made up of Chaoticity. ChaosNet uses the chaotic GLS neurons' topological transitivity property to perform classification problems with cutting-edge performance the pool of data including lower training sample count. This synthetic neural network can perform categorization tasks by gathering from a definite amount of training data. ChaosNet utilizes some of the best traits of network subjected to biological neurons, which derive from the strong Chaotic activity of individual neurons, to solve difficult classification tasks on par with or better than standard Artificial Neural Networks. It has been shown to require much fewer training samples.

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An Affiliated Approach to Data Validation: US 2020 Governor’s County Election

Roy Choudhury

2023

Studies in Big Data

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A Unified Approach to Fraudulent Detection

Cited by 11 publications

References 0 publications

Predicting Cryptocurrency Fraud Using ChaosNet: The Ethereum Manifestation

Predicting Cryptocurrency Fraud Using ChaosNet: The Ethereum Manifestation

Predicting Ethereum Fraudulency using ChaosNet

An Affiliated Approach to Data Validation: US 2020 Governor’s County Election

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