关于离链智能合约运行时保护：排队模型方法

IF 5.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-04-16 DOI:10.1109/TPDS.2024.3389153

Isra M. Ali;Mohamed M. Abdallah

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引用次数: 0

摘要

智能合约的脆弱性已通过公共区块链中越来越多的数百万美元的漏洞利用事件得到证实。有几项研究提出应用运行时验证来保护部署后的智能合约。然而，它们都没有讨论可能导致无法部署的链上开销，从而使智能合约得不到保护。解决链上开销的一个重要方法是将分析外包到链外。在这项工作中，我们分析研究了链外智能合约运行时验证的潜在效率。我们提出了链外运行时验证和区块生成过程的通用排队网络模型。排队模型方法允许我们高效灵活地捕捉区块链的非确定性行为，估计池中的交易数量及其相应的等待时间。我们分析了链上开销并评估了链下 RV，提供了交易处理延迟和吞吐量的数值指标。

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On Off-Chaining Smart Contract Runtime Protection: A Queuing Model Approach

The vulnerability of smart contracts has been demonstrated by an increasing number of multi-million exploitation incidents in public blockchains. Several works propose applying runtime verification to protect smart contracts post-deployment. However, none discuss the induced onchain overhead that may preclude its deployment, leaving smart contracts unprotected. A prominent solution to the onchain overhead is outsourcing the analysis off-chain. In this work, we analytically study the potential efficiency of off-chain smart contract runtime verification. We present a generic queueing network model of the off-chain runtime verification and the block generation process. The queuing model approach allows us to efficiently and flexibly capture the non-deterministic behavior of blockchain, estimating the number of transactions in the pool and their corresponding waiting times. We analyze the onchain overhead and evaluate off-chain RV, providing numerical indicators of transaction processing latency and throughput.

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来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

期刊介绍： IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.