A MuItilayer Perceptron Architecture for Detecting Deceptive Cryptocurrencies in Coin Market Capitalization Data

Abstract

Due to increasing popularity of Bitcoin and other cryptocurrencies, proliferation of deceptive cryptocurrencies over the internet is a global concern. In this paper, we have identified a set of 24 features through analyzing Cryptocurrency Market Capitalization (CMC) data and propose a Multilayer Perceptron (MLP) architecture for detecting deceptive cryptocurrencies. The proposed MLP architecture is compared with three traditional machine learning algorithms over a real cryptocurrency dataset crawled from CMC website, and it performs significantly better. CCS CONCEPTS • Security and privacy $\rightarrow$ Web application security; • Information systems $\rightarrow$ Data analytics; • Computing methodologies $\rightarrow$ Supervised learning.