GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection

Hui Zhao; Peng Su; Yihang Wei; Keke Gai; Meikang Qiu

doi:10.1007/978-3-030-82153-1_48

GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection

Hui Zhao, Peng Su, Yihang Wei, Keke Gai^*, Meikang Qiu

^*Corresponding author for this work

School of Cyberspace Science and Technology

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

5 Citations (Scopus)

Abstract

As one of the key components of blockchain, smart contract is playing a vital role in achieving auto-functions; however, reentrant attacks are threatening the implementation of smart contracts, which limits the adoption of blockchain systems in various scenarios. To address this issue, we propose a reentrant vulnerability detection model based on word embedding, similarity detection, and Generative Adversarial Networks (GAN). Additionally, we provide a new approach for dynamically preventing reentrant attacks. We also implement experiments to evaluate our model and results show our scheme achieves 92% detecting accuracy for reentrant attack detection.

Original language	English
Title of host publication	Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings
Editors	Han Qiu, Cheng Zhang, Zongming Fei, Meikang Qiu, Sun-Yuan Kung
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	585-597
Number of pages	13
ISBN (Print)	9783030821524
DOIs	https://doi.org/10.1007/978-3-030-82153-1_48
Publication status	Published - 2021
Event	14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021 - Tokyo, Japan Duration: 14 Aug 2021 → 16 Aug 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12817 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021
Country/Territory	Japan
City	Tokyo
Period	14/08/21 → 16/08/21

Keywords

Blockchain
Generative adversarial networks
Reentrant attack
Smart contract
Vulnerability detection

Access to Document

10.1007/978-3-030-82153-1_48

Cite this

Zhao, H., Su, P., Wei, Y., Gai, K., & Qiu, M. (2021). GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection. In H. Qiu, C. Zhang, Z. Fei, M. Qiu, & S.-Y. Kung (Eds.), Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings (pp. 585-597). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12817 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-82153-1_48

Zhao, Hui ; Su, Peng ; Wei, Yihang et al. / GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection. Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings. editor / Han Qiu ; Cheng Zhang ; Zongming Fei ; Meikang Qiu ; Sun-Yuan Kung. Springer Science and Business Media Deutschland GmbH, 2021. pp. 585-597 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "As one of the key components of blockchain, smart contract is playing a vital role in achieving auto-functions; however, reentrant attacks are threatening the implementation of smart contracts, which limits the adoption of blockchain systems in various scenarios. To address this issue, we propose a reentrant vulnerability detection model based on word embedding, similarity detection, and Generative Adversarial Networks (GAN). Additionally, we provide a new approach for dynamically preventing reentrant attacks. We also implement experiments to evaluate our model and results show our scheme achieves 92% detecting accuracy for reentrant attack detection.",

keywords = "Blockchain, Generative adversarial networks, Reentrant attack, Smart contract, Vulnerability detection",

author = "Hui Zhao and Peng Su and Yihang Wei and Keke Gai and Meikang Qiu",

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doi = "10.1007/978-3-030-82153-1_48",

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Zhao, H, Su, P, Wei, Y, Gai, K & Qiu, M 2021, GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection. in H Qiu, C Zhang, Z Fei, M Qiu & S-Y Kung (eds), Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12817 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 585-597, 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021, Tokyo, Japan, 14/08/21. https://doi.org/10.1007/978-3-030-82153-1_48

GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection. / Zhao, Hui; Su, Peng; Wei, Yihang et al.
Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings. ed. / Han Qiu; Cheng Zhang; Zongming Fei; Meikang Qiu; Sun-Yuan Kung. Springer Science and Business Media Deutschland GmbH, 2021. p. 585-597 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12817 LNAI).

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

TY - GEN

T1 - GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection

AU - Zhao, Hui

AU - Su, Peng

AU - Wei, Yihang

AU - Gai, Keke

AU - Qiu, Meikang

PY - 2021

Y1 - 2021

N2 - As one of the key components of blockchain, smart contract is playing a vital role in achieving auto-functions; however, reentrant attacks are threatening the implementation of smart contracts, which limits the adoption of blockchain systems in various scenarios. To address this issue, we propose a reentrant vulnerability detection model based on word embedding, similarity detection, and Generative Adversarial Networks (GAN). Additionally, we provide a new approach for dynamically preventing reentrant attacks. We also implement experiments to evaluate our model and results show our scheme achieves 92% detecting accuracy for reentrant attack detection.

AB - As one of the key components of blockchain, smart contract is playing a vital role in achieving auto-functions; however, reentrant attacks are threatening the implementation of smart contracts, which limits the adoption of blockchain systems in various scenarios. To address this issue, we propose a reentrant vulnerability detection model based on word embedding, similarity detection, and Generative Adversarial Networks (GAN). Additionally, we provide a new approach for dynamically preventing reentrant attacks. We also implement experiments to evaluate our model and results show our scheme achieves 92% detecting accuracy for reentrant attack detection.

KW - Blockchain

KW - Generative adversarial networks

KW - Reentrant attack

KW - Smart contract

KW - Vulnerability detection

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DO - 10.1007/978-3-030-82153-1_48

M3 - Conference contribution

AN - SCOPUS:85113741097

SN - 9783030821524

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

SP - 585

EP - 597

BT - Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings

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A2 - Zhang, Cheng

A2 - Fei, Zongming

A2 - Qiu, Meikang

A2 - Kung, Sun-Yuan

PB - Springer Science and Business Media Deutschland GmbH

T2 - 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021

Y2 - 14 August 2021 through 16 August 2021

ER -

Zhao H, Su P, Wei Y, Gai K, Qiu M. GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection. In Qiu H, Zhang C, Fei Z, Qiu M, Kung SY, editors, Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 585-597. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-82153-1_48

GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection

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Keywords

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