DEthna: Accurate Ethereum Network Topology Discovery with Marked Transactions

TL;DR

This work tackles the problem of accurately discovering Ethereum network topology, which is hard due to deliberate topology concealment. It introduces DEthna, a distributed measurement tool that employs marked transactions and a parallel probing architecture to infer node links with high accuracy and low overhead. The approach includes single and multi-link inference models and a three-layer distributed implementation, evaluated on the Goerli testnet, where DEthna outperforms baselines and reveals a prevalence of low-degree nodes that compromise network robustness. The findings provide practical insights for optimizing Ethereum block propagation and resilience, and establish a foundation for future topology-aware network protocols.

Abstract

In Ethereum, the ledger exchanges messages along an underlying Peer-to-Peer (P2P) network to reach consistency. Understanding the underlying network topology of Ethereum is crucial for network optimization, security and scalability. However, the accurate discovery of Ethereum network topology is non-trivial due to its deliberately designed security mechanism. Consequently, existing measuring schemes cannot accurately infer the Ethereum network topology with a low cost. To address this challenge, we propose the Distributed Ethereum Network Analyzer (DEthna) tool, which can accurately and efficiently measure the Ethereum network topology. In DEthna, a novel parallel measurement model is proposed that can generate marked transactions to infer link connections based on the transaction replacement and propagation mechanism in Ethereum. Moreover, a workload offloading scheme is designed so that DEthna can be deployed on multiple distributed probing nodes so as to measure a large-scale Ethereum network at a low cost. We run DEthna on Goerli (the most popular Ethereum test network) to evaluate its capability in discovering network topology. The experimental results demonstrate that DEthna significantly outperforms the state-of-the-art baselines. Based on DEthna, we further analyze characteristics of the Ethereum network revealing that there exist more than 50% low-degree Ethereum nodes that weaken the network robustness.

Figures (9)

• Figure 1: Single Link inference model to infer a link between nodes $A$ and $B$: (a) $TX_A$, $TX_B$ and $TX_C$ with nonce $n+1$ are regarded as future transactions and not forwarded over the network before $TX_M$ with nonce $n$ is sent out; (b) $TX_A$, $TX_B$ obeying P1, P2&P3, and $TX_C$ are transformed to pending transactions and forwarded after $TX_M$ arrives, and $TX_B$ can arrive at node $A$ only when there is a link between nodes $A$ and $B$.
• Figure 2: Multi-link inference model: at Step 1, three marked transactions with the same nonce but different effective prices in each column are used to infer a link. $k$ marked transactions with continuous nonces and the same effective price in each row are sent to different nodes; at Step 2, $TX_M$ is sent to nodes $C$, which are the nodes in the network except $A_k$ and $B_k$.
• Figure 3: The workflow when there exist nodes $C$’ not connecting with node $M$ can lead to the absence of $TX_C$. $TX_B$ is still isolated by nodes $C$’ when $TX_C$ arrives at node $C'$ earlier than $TX_B$.
• Figure 4: The distributed measurement architecture consisting of 9 modified Ethereum nodes located around the world to act as node $M$.
• Figure 5: The network size of Goerli measured by DEthna and Basic TopoShot.