[论文解读] Stochastic Modeling Approaches for Analyzing Blockchain: A Survey

Hongyue Kang, Xiaolin Chang+4 more|arXiv (Cornell University)|Sep 13, 2020

Blockchain Technology Applications and Security参考文献 70被引用 6

一句话总结

本综述全面回顾了用于分析区块链系统的随机建模方法，将其分类为面向网络的（性能与安全）模型和面向应用的（主要为加密货币价格预测）模型。该综述评估了现有方法，比较了其优缺点，并识别出关键挑战与未来研究方向，以指导利用随机技术进行区块链分析的进一步研究。

ABSTRACT

Blockchain technology has been attracting much attention from both academia and industry. It brings many benefits to various applications like Internet of Things. However, there are critical issues to be addressed before its widespread deployment, such as transaction efficiency, bandwidth bottleneck, and security. Techniques are being explored to tackle these issues. Stochastic modeling, as one of these techniques, has been applied to analyze a variety of blockchain characteristics, but there is a lack of a comprehensive survey on it. In this survey, we aim to fill the gap and review the stochastic models proposed to address common issues in blockchain. Firstly, this paper provides the basic knowledge of blockchain technology and stochastic models. Then, according to different objects, the stochastic models for blockchain analysis are divided into network-oriented and application-oriented (mainly refer to cryptocurrency). The network-oriented stochastic models are further classified into two categories, namely, performance and security. About the application-oriented stochastic models, the widest adoption mainly concentrates on the price prediction of cryptocurrency. Moreover, we provide analysis and comparison in detail on every taxonomy and discuss the strengths and weaknesses of the related works to serve guides for further researches. Finally, challenges and future research directions are given to apply stochastic modeling approaches to study blockchain. By analyzing and classifying the existing researches, we hope that our survey can provide suggestions for the researchers who are interested in blockchain and good at using stochastic models as a tool to address problems.

研究动机与目标

• 为解决区块链分析中随机建模领域缺乏全面综述的问题。
• 根据应用重点对随机模型进行分类与分析：面向网络的（性能与安全）和面向应用的（加密货币价格预测）。
• 比较现有随机模型的优势与劣势，以指导未来研究。
• 识别开放性挑战，并提出在区块链系统中应用随机建模的未来研究方向。

提出的方法

• 基于目标将随机模型系统性地划分为面向网络的和面向应用的类别。
• 将面向网络的模型进一步细分为性能与安全子类，重点关注交易吞吐量、延迟以及对攻击的鲁棒性。
• 调查面向应用的模型，特别是用于基于随机过程的加密货币价格预测的模型。
• 通过详细分析与比较评估模型方法论，突出其假设、局限性与适用性。
• 采用分类学驱动的方法，按问题领域、技术与用例对模型进行组织。
• 提供对模型设计选择及其对区块链系统评估影响的深入见解。

实验结果

研究问题

• RQ1 区块链分析中使用的随机模型主要有哪些类别，它们在结构与目的上如何不同？
• RQ2 面向网络的随机模型如何应对区块链系统中的性能与安全挑战？
• RQ3 随机模型在预测加密货币价格动态方面已取得多大程度的成功？
• RQ4 现有区块链随机建模方法中的关键局限性与假设是什么？
• RQ5 在区块链中推进随机建模方面，哪些未来研究方向最具前景？

主要发现

• 随机建模被广泛用于分析区块链性能，特别是在建模交易到达过程与区块传播延迟方面。
• 面向安全的随机模型能有效模拟诸如遮蔽攻击与自私挖矿等对抗性行为，为攻击概率与系统韧性提供洞见。
• 基于几何布朗运动与跳跃扩散过程等随机过程的加密货币价格预测模型表现出中等预测能力，但对模型假设高度敏感。
• 在标准化评估框架方面存在显著空白，导致不同研究之间的比较缺乏一致性。
• 许多现有模型过度简化了现实中的区块链动态，尤其是在建模网络异构性与自适应攻击者行为方面。
• 未来研究应聚焦于整合多尺度随机模型，以同时捕捉微观层面的交易行为与宏观层面的网络动态。