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K-Nearest Neighbors Algorithm (KNN): An Approach to Detect Illicit Transaction in the Bitcoin Network
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Author(s): Abdelaziz Elbaghdadi (Abdelmalek Essaadi University, Morocco), Soufiane Mezroui (Abdelmalek Essaadi University, Morocco)and Ahmed El Oualkadi (Abdelmalek Essaadi University, Morocco)
Copyright: 2021
Pages: 18
Source title:
Integration Challenges for Analytics, Business Intelligence, and Data Mining
Source Author(s)/Editor(s): Ana Azevedo (CEOS.PP, ISCAP, Polytechnic of Porto, Portugal)and Manuel Filipe Santos (Algoritmi Centre, University of Minho, Guimarães, Portugal)
DOI: 10.4018/978-1-7998-5781-5.ch008
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Abstract
The cryptocurrency is the first implementation of blockchain technology. This technology provides a set of tracks and innovation in scientific research, such as use of data either to detect anomalies either to predict price in the Bitcoin and the Ethereum. Furthermore, the blockchain technology provide a set of technique to automate the business process. This chapter presents a review of some research works related to cryptocurrency. A model with a KNN algorithm is proposed to detect illicit transaction. The proposed model uses both the elliptic dataset and KNN algorithm to detect illicit transaction. Furthermore, the elliptic dataset contains 203,769 nodes and 234,355 edges; it allows to classify the data into three classes: illicit, licit, or unknown. Each node has associated 166 features. The first 94 features represent local information about the transaction. The remaining 72 features are called aggregated features. The accuracy exceeded 90% with k=2 and k=4, the recall reaches 56% with k=3, and the precision reaches 78% with k=4.
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