Eclipse Attack Detection on a Blockchain Network as a Non-Parametric Change Detection Problem

TL;DR

This paper presents a new non-parametric change detection algorithm for identifying eclipse attacks on blockchain networks, using statistical properties of network data and capable of being implemented as a tamper-proof smart contract.

Contribution

The paper introduces a novel non-parametric detection method based on empirical mean and variance, utilizing the Johnson-Lindenstrauss lemma and weak convergence analysis for blockchain security.

Findings

Effective detection of eclipse attacks demonstrated through numerical examples.

The proposed detector outperforms random forest-based methods in accuracy.

Implementation as a tamper-proof smart contract enhances security and reliability.

Abstract

This paper introduces a novel non-parametric change detection algorithm to identify eclipse attacks on a blockchain network; the non-parametric algorithm relies only on the empirical mean and variance of the dataset, making it highly adaptable. An eclipse attack occurs when malicious actors isolate blockchain users, disrupting their ability to reach consensus with the broader network, thereby distorting their local copy of the ledger. To detect an eclipse attack, we monitor changes in the Fr\'echet mean and variance of the evolving blockchain communication network connecting blockchain users. First, we leverage the Johnson-Lindenstrauss lemma to project large-dimensional networks into a lower-dimensional space, preserving essential statistical properties. Subsequently, we employ a non-parametric change detection procedure, leading to a test statistic that converges weakly to a Brownian bridge process in the absence of an eclipse attack. This enables us to quantify the false alarm rate of the detector. Our detector can be implemented as a smart contract on the blockchain, offering a tamper-proof and reliable solution. Finally, we use numerical examples to compare the proposed eclipse attack detector with a detector based on the random forest model.