A Survey on Safety Regulation Technology of Blockchain Application and Blockchain Ecology

Shen, Pengxu; Li, Suozai; Huang, Ming; Gao, Haoyu; Li, Leixiao; Li, Jun; Lei, Hong

doi:10.1109/blockchain55522.2022.00076

Cited by 20 publications

(23 citation statements)

References 7 publications

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“…There are a total of 143 opcode instructions available in smart contracts [2] . While some opcode instructions have similar functions, they may differ slightly in their symbolic representation.…”

Section: B Opcode Instructions Clusteringmentioning

confidence: 99%

“…For instance, PUSH1, PUSH2 and PUSH3 are all instructions used for pushing values onto the stack, but they differ in the number of bytes they push. Furthermore, it [2] Ethervm: https://www.ethervm.io/ Fig. 2: Framework of OC-Detector.…”

Section: B Opcode Instructions Clusteringmentioning

confidence: 99%

“…For instance, in 2016, a security vulnerability in the DAO contracts led to the loss of more than 3.6 million Ethereum tokens, with a value of about $60 million. In this case, the attacker repeatedly reentered the transfer function to steal Ethereum tokens through a reentrance vulnerability in the DAO contract [2].…”

Section: Introductionmentioning

confidence: 99%

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Smart Contract Vulnerability Detection Based on Clustering Opcode Instructions

Gu,

Yang,

Liu

et al. 2023

International Conferences on Software Engineering and Knowledge Engineering

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Smart contracts are programs running on the blockchain. In recent years, due to the continuous occurrence of smart contract security accidents, how to effectively detect vulnerabilities in smart contracts has received extensive attention. Machine learning-based vulnerability detection techniques have the advantage of not requiring expert rules. However, existing approaches have limitations in identifying vulnerabilities caused by version updates of smart contract compilers. In this paper, we propose OC-Detector, a smart contract vulnerabilities detection approach based on opcode instruction clustering. OC-Detector learns the characteristics of opcode instructions to cluster them and replaces opcode instructions belonging to the same cluster with the cluster number. After that, the similarity is calculated against the contract in the vulnerability database to identify vulnerabilities. Experimental results demonstrate that OC-Detector improves the F 1 value of detecting vulnerabilities from 0.04 to 0.40 compared to DC-Hunter, Securify, SmartCheck, and Osiris. Additionally, compared to DC-Hunter, F 1 value is improved by 0.27 when detecting vulnerabilities in smart contracts compiled by different version compilers.

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Section: B Opcode Instructions Clusteringmentioning

confidence: 99%

Section: B Opcode Instructions Clusteringmentioning

confidence: 99%

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Smart Contract Vulnerability Detection Based on Clustering Opcode Instructions

Gu,

Yang,

Liu

et al. 2023

International Conferences on Software Engineering and Knowledge Engineering

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“…Blockchains can be run on custom hardware, can be configured as needed, can enforce a permission concept to keep data private, and they can offer different architectures according to a specific use case. Given the vast number of options available, it's challenging to predict expected performance.Vendors often tout remarkable performance [8,16,18,48,55], but there is lack of a fair comparison of systems under comparable conditions and workloads. As the permissioned blockchain systems widely differ in their design, benchmarking them is difficult.…”

Section: Introductionmentioning

confidence: 99%

“…To close this gap, we are conducting a performance evaluation of seven systems with a specially developed benchmarking system based on selected load scenarios. The seven systems comprise Corda Open Source (OS) [23,43], Corda Enterprise [23,43], BitShares [3,28] which is based on the Graphene blockchain framework [36], Hyperledger Fabric (Fabric) [6,16], Quorum [12,22], Hyperledger Sawtooth (Sawtooth) [7,41] and Diem [5,55].…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Performance Comparison of Seven Permissioned Blockchain Systems

Geyer,

Jacobsen,

Mayer

et al. 2023

Proceedings of the 24th International Middleware Conference on ZZZ

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The emergence of numerous blockchain solutions, offering innovative approaches to optimise performance, scalability, privacy, and governance, complicates performance analysis. Reasons for the difficulty of benchmarking blockchains include, for example, the high number of system parameters to configure and the effort to deploy a blockchain network. In addition, performance data, which mostly comes from system vendors, is often opaque. We provide a reproducible evaluation of the performance of seven permissioned blockchain systems across different parameter settings. We employ an end-to-end approach, where the clients sending the transactions are fully involved in the data collection approach. Our results underscore the unique characteristics and limitations of the systems we examined. Due to the insights given, our work forms the basis for continued research to optimise the performance of blockchain systems. CCS CONCEPTS• Computer systems organization → Peer-to-peer architectures.

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