Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric

Pengfei Huang; Wanqing Jie; Arthur Sandor Voundi Koe; Ruitao Hou; Hongyang Yan; Mourad Nouioua; Phan Duc Thien; Jacques Mbous Ikong; Camara Lancine

doi:10.1007/978-3-031-20099-1_39

Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric

Pengfei Huang, Wanqing Jie, Arthur Sandor Voundi Koe^*, Ruitao Hou, Hongyang Yan, Mourad Nouioua, Phan Duc Thien, Jacques Mbous Ikong, Camara Lancine

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Blockchain testing plays a critical role in the maturation of blockchain technology by ensuring the quality of implemented functional and non-functional requirements. In the new global economy, rapid time to market has become a central issue: developers fail to scrutinize their blockchain designs prior to deployment and customers undergo negative experiences that hurt the widespread adoption of the blockchain technology. Previous published studies aimed for effective blockchain simulators. However, existing solutions exhibit several drawbacks: they rely on guesswork, conceal low-level implementation details, lack expected realistic outcomes and automated testing, as well as lag in smart contract vulnerability analysis. In this paper, we introduce highsimb: the first concrete blockchain high simulation platform for Ethereum and Hyperledger Fabric that supports smart contract vulnerability detection. Unlike a testnet, the blockchain tester can customize any low-level detail to achieve realistic expected results under automated testing. Theoretical analysis demonstrates our concrete simulator is highly observable, supports realistic feedback, is scalable, detects smart contract vulnerabilities, has strong white-box testing capabilities and automates experiments. Our framework complements existing blockchain simulators and introduces a novel development paradigm for blockchain testing.

Original language	English
Title of host publication	Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings
Editors	Yuan Xu, Hongyang Yan, Huang Teng, Jun Cai, Jin Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	455-468
Number of pages	14
ISBN (Print)	9783031200984
DOIs	https://doi.org/10.1007/978-3-031-20099-1_39
State	Published - 2023
Externally published	Yes
Event	4th International Conference on Machine Learning for Cyber Security, ML4CS 2022 - Guangzhou, China Duration: 2 Dec 2022 → 4 Dec 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13656 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Conference on Machine Learning for Cyber Security, ML4CS 2022
Country/Territory	China
City	Guangzhou
Period	2/12/22 → 4/12/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Bitcoin
Blockchain
Ethereum
Hyperledger Fabric
Simulator

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-20099-1_39

Cite this

Huang, P., Jie, W., Voundi Koe, A. S., Hou, R., Yan, H., Nouioua, M., Thien, P. D., Mbous Ikong, J., & Lancine, C. (2023). Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric. In Y. Xu, H. Yan, H. Teng, J. Cai, & J. Li (Eds.), Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings (pp. 455-468). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13656 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-20099-1_39

Huang, Pengfei ; Jie, Wanqing ; Voundi Koe, Arthur Sandor et al. / Highsimb : A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric. Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings. editor / Yuan Xu ; Hongyang Yan ; Huang Teng ; Jun Cai ; Jin Li. Springer Science and Business Media Deutschland GmbH, 2023. pp. 455-468 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ac8a951bdd3b47ce89b9b380f80f71a2,

title = "Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric",

abstract = "Blockchain testing plays a critical role in the maturation of blockchain technology by ensuring the quality of implemented functional and non-functional requirements. In the new global economy, rapid time to market has become a central issue: developers fail to scrutinize their blockchain designs prior to deployment and customers undergo negative experiences that hurt the widespread adoption of the blockchain technology. Previous published studies aimed for effective blockchain simulators. However, existing solutions exhibit several drawbacks: they rely on guesswork, conceal low-level implementation details, lack expected realistic outcomes and automated testing, as well as lag in smart contract vulnerability analysis. In this paper, we introduce highsimb: the first concrete blockchain high simulation platform for Ethereum and Hyperledger Fabric that supports smart contract vulnerability detection. Unlike a testnet, the blockchain tester can customize any low-level detail to achieve realistic expected results under automated testing. Theoretical analysis demonstrates our concrete simulator is highly observable, supports realistic feedback, is scalable, detects smart contract vulnerabilities, has strong white-box testing capabilities and automates experiments. Our framework complements existing blockchain simulators and introduces a novel development paradigm for blockchain testing.",

keywords = "Bitcoin, Blockchain, Ethereum, Hyperledger Fabric, Simulator",

author = "Pengfei Huang and Wanqing Jie and \{Voundi Koe\}, \{Arthur Sandor\} and Ruitao Hou and Hongyang Yan and Mourad Nouioua and Thien, \{Phan Duc\} and \{Mbous Ikong\}, Jacques and Camara Lancine",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 4th International Conference on Machine Learning for Cyber Security, ML4CS 2022 ; Conference date: 02-12-2022 Through 04-12-2022",

year = "2023",

doi = "10.1007/978-3-031-20099-1\_39",

language = "English",

isbn = "9783031200984",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "455--468",

editor = "Yuan Xu and Hongyang Yan and Huang Teng and Jun Cai and Jin Li",

booktitle = "Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings",

address = "Germany",

}

Huang, P, Jie, W, Voundi Koe, AS, Hou, R, Yan, H, Nouioua, M, Thien, PD, Mbous Ikong, J & Lancine, C 2023, Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric. in Y Xu, H Yan, H Teng, J Cai & J Li (eds), Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13656 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 455-468, 4th International Conference on Machine Learning for Cyber Security, ML4CS 2022, Guangzhou, China, 2/12/22. https://doi.org/10.1007/978-3-031-20099-1_39

Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric. / Huang, Pengfei; Jie, Wanqing; Voundi Koe, Arthur Sandor et al.
Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings. ed. / Yuan Xu; Hongyang Yan; Huang Teng; Jun Cai; Jin Li. Springer Science and Business Media Deutschland GmbH, 2023. p. 455-468 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13656 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Highsimb

T2 - 4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

AU - Huang, Pengfei

AU - Jie, Wanqing

AU - Voundi Koe, Arthur Sandor

AU - Hou, Ruitao

AU - Yan, Hongyang

AU - Nouioua, Mourad

AU - Thien, Phan Duc

AU - Mbous Ikong, Jacques

AU - Lancine, Camara

PY - 2023

Y1 - 2023

N2 - Blockchain testing plays a critical role in the maturation of blockchain technology by ensuring the quality of implemented functional and non-functional requirements. In the new global economy, rapid time to market has become a central issue: developers fail to scrutinize their blockchain designs prior to deployment and customers undergo negative experiences that hurt the widespread adoption of the blockchain technology. Previous published studies aimed for effective blockchain simulators. However, existing solutions exhibit several drawbacks: they rely on guesswork, conceal low-level implementation details, lack expected realistic outcomes and automated testing, as well as lag in smart contract vulnerability analysis. In this paper, we introduce highsimb: the first concrete blockchain high simulation platform for Ethereum and Hyperledger Fabric that supports smart contract vulnerability detection. Unlike a testnet, the blockchain tester can customize any low-level detail to achieve realistic expected results under automated testing. Theoretical analysis demonstrates our concrete simulator is highly observable, supports realistic feedback, is scalable, detects smart contract vulnerabilities, has strong white-box testing capabilities and automates experiments. Our framework complements existing blockchain simulators and introduces a novel development paradigm for blockchain testing.

AB - Blockchain testing plays a critical role in the maturation of blockchain technology by ensuring the quality of implemented functional and non-functional requirements. In the new global economy, rapid time to market has become a central issue: developers fail to scrutinize their blockchain designs prior to deployment and customers undergo negative experiences that hurt the widespread adoption of the blockchain technology. Previous published studies aimed for effective blockchain simulators. However, existing solutions exhibit several drawbacks: they rely on guesswork, conceal low-level implementation details, lack expected realistic outcomes and automated testing, as well as lag in smart contract vulnerability analysis. In this paper, we introduce highsimb: the first concrete blockchain high simulation platform for Ethereum and Hyperledger Fabric that supports smart contract vulnerability detection. Unlike a testnet, the blockchain tester can customize any low-level detail to achieve realistic expected results under automated testing. Theoretical analysis demonstrates our concrete simulator is highly observable, supports realistic feedback, is scalable, detects smart contract vulnerabilities, has strong white-box testing capabilities and automates experiments. Our framework complements existing blockchain simulators and introduces a novel development paradigm for blockchain testing.

KW - Bitcoin

KW - Blockchain

KW - Ethereum

KW - Hyperledger Fabric

KW - Simulator

UR - https://www.scopus.com/pages/publications/85148683316

U2 - 10.1007/978-3-031-20099-1_39

DO - 10.1007/978-3-031-20099-1_39

M3 - Conference contribution

AN - SCOPUS:85148683316

SN - 9783031200984

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 455

EP - 468

BT - Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings

A2 - Xu, Yuan

A2 - Yan, Hongyang

A2 - Teng, Huang

A2 - Cai, Jun

A2 - Li, Jin

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 2 December 2022 through 4 December 2022

ER -

Huang P, Jie W, Voundi Koe AS, Hou R, Yan H, Nouioua M et al. Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric. In Xu Y, Yan H, Teng H, Cai J, Li J, editors, Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 455-468. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-20099-1_39