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.
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Notes
- 1.
The BOLTs (Basis Of Lightning Technology) are specifications for the LN, used to ensure that different Lightning Network node implementations can interact seamlessly.
- 2.
Although these nodes currently share this information voluntarily, the data collected in this project in mainnet network environment will not be published, to prevent this critical information from being perpetuated over time.
- 3.
This corresponds to all nodes that inform their implementation and accepted our incoming P2P connections.
- 4.
These features correspond to data-loss-protect (0), tlv-onion (9), static-remote-key (12), amp (31), and script-enforced-lease (2023), for LND; unknown (39), for Eclair; and scid-alias (47), zero-conf (51), and keysend (55), for CLN .
- 5.
The similarity is calculated based on the intersection of two lists with the attributes of each implementation.
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Acknowledgements
This work has been partially supported by the Spanish ministry under grants PID2021-125962OB-C33 SECURING/NET and PID2021-125962OB-C31 SECURING/CYBER; the “Plan de Recuperación, Transformación y Resiliencia” funded by the European Union - NextGenerationEU under the project DANGER C062/23; and the AGAUR grants SGR2021-00643 and SGR2021-01508.
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Espinasa-Vilarrasa, P., Sanvicente, S., Pérez-Solà, C., Herrera-Joancomartí, J. (2024). Decision Tree Based Inference of Lightning Network Client Implementations. In: Torra, V., Narukawa, Y., Kikuchi, H. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2024. Lecture Notes in Computer Science(), vol 14986. Springer, Cham. https://doi.org/10.1007/978-3-031-68208-7_9
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