TY - JOUR
T1 - Decision Tree Based Inference of Lightning Network Client Implementations
AU - Espinasa-Vilarrasa, Pol
AU - Sanvicente, Sílvia
AU - Pérez-Solà, Cristina
AU - Herrera-Joancomartí, Jordi
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Bitcoin
KW - Blockchain
KW - Lightning Network (LN)
KW - Machine Learning
UR - https://www.scopus.com/pages/publications/85202190090
U2 - 10.1007/978-3-031-68208-7_9
DO - 10.1007/978-3-031-68208-7_9
M3 - Article
AN - SCOPUS:85202190090
SN - 0302-9743
SP - 103
EP - 114
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -