区块链网络上的日蚀攻击检测作为一个非参数变点检测问题
Eclipse Attack Detection on a Blockchain Network as a Non-Parametric Change Detection Problem
AI总结：
本文提出一种非参数变点检测算法，通过监控区块链通信网络Fréchet均值和方差的变化，并利用Johnson-Lindenstrauss引理降维，实现可作智能合约部署的日蚀攻击检测。
摘要：
本文引入一种新颖的非参数变点检测算法来识别区块链网络上的日蚀攻击；该非参数算法仅依赖数据集的经验均值和方差，使其具有高度适应性。日蚀攻击发生在恶意行为者隔离区块链用户时，破坏他们与更广泛网络达成共识的能力，从而扭曲其本地的账本副本。为了检测日蚀攻击，我们监控连接区块链用户的不断演变的区块链通信网络的 Fréchet 均值和方差的变化。首先，我们利用 Johnson-Lindenstrauss 引理将大维网络投影到低维空间中，保留基本的统计特性。随后，我们采用非参数变点检测程序，得到一个在无日蚀攻击时弱收敛于 Brownian bridge 过程的检验统计量。这使我们能够量化检测器的误报率。我们的检测器可以作为区块链上的智能合约实现，提供防篡改且可靠的解决方案。最后，我们使用数值例子将提出的日蚀攻击检测器与基于 random forest 模型的检测器进行比较。
英文摘要：
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 Fréchet 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.
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