Robust and Automated Reconfiguration of Byzantine Wide-Area Replication

2026-06-15

AI Summary

Existing Byzantine fault-tolerant state machine replication systems are vulnerable to exploitation by malicious nodes during dynamic reconfiguration, leading to degraded security and performance. This work proposes Beware, a novel framework that uniquely integrates robust statistics with machine learning to enhance resilience. By employing anomaly detection to filter falsified latency reports, computing Byzantine-robust voting weights, and automatically converging to an optimal system configuration, Beware effectively mitigates attacks such as latency spoofing. Evaluated in wide-area network environments, the approach reduces consensus latency by up to 45% while significantly improving both system security and performance.

Abstract

Distributed systems handle adversarial nodes through redundancy, which imposes a significant performance overhead. In blockchain systems, Byzantine fault-tolerant state-machine replication (BFT-SMR) is the replicated service that totally orders client transactions before execution. While prior research has primarily focused on designing novel consensus algorithms with improved performance, recent studies have shown that further gains can be achieved through configuration optimization. More precisely, replicas can monitor network latency to dynamically assign the leader role and tune voting weights, thereby improving consensus performance. However, we identify three vulnerabilities in this process that Byzantine nodes can exploit. To address these weaknesses, we propose Beware, a reconfiguration framework that filters out falsified latency reports, computes robust weight distributions, and applies machine learning to converge towards Byzantine-resilient configurations. Our evaluation shows that Beware reduces consensus latency by up to 45% compared to existing solutions.

Problem

• Byzantine fault tolerance
• state-machine replication
• reconfiguration
• consensus latency
• distributed systems

Innovation

• Byzantine fault tolerance
• state-machine replication
• dynamic reconfiguration
• robust weight assignment
• machine learning