Non-Intrusive Balance Tomography Using Reinforcement Learning in the Lightning Network

Author:

Qiao Yan1,Wu Kui2,Khabbazian Majid3

Affiliation:

1. School of Computer Science and Information Engineering, and Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, China

2. Department of Computer Science, University of Victoria, Canada

3. Department of Electrical and Computer Engineering, University of Alberta, Canada

Abstract

The Lightning Network (LN) is a second layer system for solving the scalability problem of Bitcoin transactions. In the current implementation of LN, channel capacity (i.e., the sum of individual balances held in the channel) is public information, while individual balances are kept secret for privacy concerns. Attackers may discover a particular balance of a channel by sending multiple fake payments through the channel. Such an attack, however, can hardly threaten the security of the LN system due to its high cost and noticeable intrusions. In this work, we present a novel non-intrusive balance tomography attack, which infers channel balances silently by performing legal transactions between two pre-created LN nodes. To minimize the cost of the attack, we propose an algorithm to compute the optimal payment amount for each transaction and design a path construction method using reinforcement learning to explore the most informative path to conduct the transactions. Finally, we propose two approaches (NIBT-RL and NIBT-RL- β ) to accurately and efficiently infer all individual balances using the results of these transactions. Experiments using simulated account balances over actual LN topology show that our method can accurately infer \(90\%\sim 94\% \) of all balances in LN with around 12 USD.

Publisher

Association for Computing Machinery (ACM)

Subject

Safety, Risk, Reliability and Quality,General Computer Science

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