Threat Adaptive Byzantine Fault Tolerant State-Machine Replication

Douglas Simões Silva, Rafal Graczyk, Jérémie Decouchant, Marcus Völp, Paulo Esteves-Verissimo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Critical infrastructures have to withstand advanced and persistent threats, which can be addressed using Byzantine fault tolerant state-machine replication (BFT-SMR). In practice, unattended cyberdefense systems rely on threat level detectors that synchronously inform them of changing threat levels. However, to have a BFT-SMR protocol operate unattended, the state-of-the-art is still to configure them to withstand the highest possible number of faulty replicas $f$ they might encounter, which limits their performance, or to make the strong assumption that a trusted external reconfiguration service is available, which introduces a single point of failure. In this work, we present ThreatAdaptive the first BFT-SMR protocol that is automatically strengthened or optimized by its replicas in reaction to threat level changes. We first determine under which conditions replicas can safely reconfigure a BFT-SMR system, i.e., adapt the number of replicas $n$ and the fault threshold $f$ so as to outpace an adversary. Since replicas typically communicate with each other using an asynchronous network they cannot rely on consensus to decide how the system should be reconfigured. ThreatAdaptive avoids this pitfall by proactively preparing the reconfiguration that may be triggered by an increasing threat when it optimizes its performance. Our evaluation shows that ThreatAdaptive can meet the latency and throughput of BFT baselines configured statically for a particular level of threat, and adapt 30% faster than previous methods, which make stronger assumptions to provide safety.
Original languageEnglish (US)
Title of host publication2021 40th International Symposium on Reliable Distributed Systems (SRDS)
PublisherIEEE
ISBN (Print)978-1-6654-3820-9
DOIs
StatePublished - Nov 22 2021

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