Decision tree based inference of lightning network client implementations
Espinasa Vilarrasa, Pol (Universitat Autònoma de Barcelona)
Sanvicente, Sílvia
Pérez-Solà, Cristina (Universitat Autònoma de Barcelona)
Herrera-Joancomartí, Jordi (Universitat Autònoma de Barcelona)

Date:	2024
Description:	12 pàg.
Abstract:	The Lightning Network (LN) is a second layer payment protocol on top of Bitcoin. It creates a peer-to-peer (P2P) network of payment channels that enable instant payments. The LN can be accessed through different implementations or clients, the most popular being Lightning Network Daemon (LND), Core Lightning Network (CLN), and Eclair. The first step in many known attacks to the LN is to infer the software client the node is running. This paper presents two classification models based on decision trees to infer the implementation of LN clients from either the traffic of the gossip protocol or the announced BOLT #9 features, offering a cost-free means of identification. The accuracy presented by both models in our experiments is high, ranging from 87% to 100% depending on the model and the environment where it is deployed. The application of our inference models on the LN shows a prevalence of LND clients.
Grants:	Agencia Estatal de Investigación PID2021-125962OB-C33 Agencia Estatal de Investigación PID2021-125962OB-C31 Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-01508 Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-00643
Note:	This work has been partially funded by the European Union - NextGenerationEU under the project DANGER C062/23.
Rights:	Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.
Language:	Anglès
Series:	Lecture notes in computer science ; 14986
Document:	Capítol de llibre ; recerca ; Versió acceptada per publicar
Subject:	Bitcoin ; Blockchain ; Lightning Network (LN) ; Machine Learning
Published in:	Modeling Decisions for Artificial Intelligence - MDAI 2024, 2024, p. 103-114, ISBN 978-3-031-68208-7

DOI: 10.1007/978-3-031-68208-7_9

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