Non-intrusive Balance Tomography Using Reinforcement Learning in the Lightning Network
Bibliographic record
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% ∼ 94% of all balances in LN with around 12 USD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".