SCOOP: CoSt-effective COngestiOn Attacks in Payment Channel Networks

2025-03-16

AI Summary

Payment Channel Networks (PCNs) exhibit critical security blind spots under congestion attacks, particularly in resource-constrained scenarios. Method: We propose two novel, low-cost congestion attacks—budget-constrained targeted congestion and congestion-maximization—formulating the joint optimization of path minimum transferable amount and channel count as linear programs under path capacity and length constraints. Our approach integrates linear programming modeling with game-theoretic attack strategy simulation to optimize either congestion impact for a fixed budget or minimal budget required to achieve a target congestion level. Contribution/Results: Experiments demonstrate that our framework improves congestion effectiveness by 40–50% and achieves 60–90% higher congestion efficiency per unit budget compared to state-of-the-art attacks. These results critically expose structural vulnerabilities in PCN routing protocols when facing adversarial resource constraints.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

Payment channel networks (PCNs) are a promising solution to address blockchain scalability and throughput challenges, However, the security of PCNs and their vulnerability to attacks are not sufficiently studied. In this paper, we introduce SCOOP, a framework that includes two novel congestion attacks on PCNs. These attacks consider the minimum transferable amount along a path (path capacity) and the number of channels involved (path length), formulated as linear optimization problems. The first attack allocates the attacker's budget to achieve a specific congestion threshold, while the second maximizes congestion under budget constraints. Simulation results show the effectiveness of the proposed attack formulations in comparison to other attack strategies. Specifically, the results indicate that the first attack provides around a 40% improvement in congestion performance, while the second attack offers approximately a 50% improvement in comparison to the state-of-the-art. Moreover, in terms of payment to congestion efficiency, the first attack is about 60% more efficient, and the second attack is around 90% more efficient in comparison to state-of-the-art

Problem

• Analyzes vulnerabilities in Payment Channel Networks (PCNs).
• Introduces SCOOP framework for congestion attacks on PCNs.
• Optimizes attack strategies to maximize congestion and efficiency.

Innovation

• Introduces SCOOP framework for PCN congestion attacks
• Formulates attacks as linear optimization problems
• Demonstrates significant efficiency improvements over existing methods