Web3 Sybil avoidance using network latency

Abstract

Web3 is emerging as the new Internet-interaction model that facilitates direct collaboration between strangers without a need for prior trust between network participants and without central authorities. However, one of its shortcomings is the lack of a defense mechanism against the ability of a single user to generate a surplus of identities, known as the Sybil attack. Web3 has a Sybil attack problem because it uses peer sampling to establish connections between users. We evaluate the promising but under-explored direction of Sybil avoidance using network latency measurements, according to which two identities with equal latencies are suspected to be operated from the same node, and thus are likely Sybils. Network latency measurements have two desirable properties: they are only malleable by attackers by adding latency, and they do not require any trust between network participants. Our basic SybilSys mechanism avoids Sybil attackers using only network latency measurements if attackers do not actively exploit their malleability. We present an enhanced version of SybilSys that protects against targeted attacks using a variant of the flow correlation attack, which we name TrafficJamTrigger. We show how the message flows of Round-Trip Time measurements can be used to expose attack patterns and we propose and evaluate six classifiers to recognize these patterns. Our experiments show, through both emulation and real-world deployment, that enhanced SybilSys can serve a fundamental role for Web3, effectively establishing connections to real users even in the face of networks consisting of 99% Sybils.