Predicting Cryptocurrency Fraud Using ChaosNet: The Ethereum Manifestation

Anurag Dutta, Liton Chandra Voumik (), Athilingam Ramamoorthy, Samrat Ray and Asif Raihan
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Anurag Dutta: Department of Computer Science, Government College of Engineering and Textile Technology, Serampore, Calcutta 712201, India
Liton Chandra Voumik: Department of Economics, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
Athilingam Ramamoorthy: Department of Mathematics, Velammal Engineering College, Anna University, Chennai 600066, India
Samrat Ray: The Institute of Industrial Management, Economics and Trade, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 190005, Russia
Asif Raihan: Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia

JRFM, 2023, vol. 16, issue 4, 1-13

Abstract: 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.

Keywords: Cryptocurrency; blockchain; ChaosNet; GLS Neurons; Artificial Neural Network (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
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