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

^*此作品的通讯作者

网络空间安全学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

5 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings
编辑	Han Qiu, Cheng Zhang, Zongming Fei, Meikang Qiu, Sun-Yuan Kung
出版商	Springer Science and Business Media Deutschland GmbH
页	585-597
页数	13
ISBN（印刷版）	9783030821524
DOI	https://doi.org/10.1007/978-3-030-82153-1_48
出版状态	已出版 - 2021
活动	14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021 - Tokyo, 日本期限: 14 8月 2021 → 16 8月 2021

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	12817 LNAI
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021
国家/地区	日本
市	Tokyo
时期	14/08/21 → 16/08/21

访问文件

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

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhao, H., Su, P., Wei, Y., Gai, K., & Qiu, M. (2021). GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection. 在 H. Qiu, C. Zhang, Z. Fei, M. Qiu, & S.-Y. Kung (编辑), Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings (页码 585-597). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 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 等. / GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection. Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings. 编辑 / Han Qiu ; Cheng Zhang ; Zongming Fei ; Meikang Qiu ; Sun-Yuan Kung. Springer Science and Business Media Deutschland GmbH, 2021. 页码 585-597 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0e10635ddf4249519f232f2b339a47e0,

title = "GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection",

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",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021 ; Conference date: 14-08-2021 Through 16-08-2021",

year = "2021",

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

language = "English",

isbn = "9783030821524",

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 = "585--597",

editor = "Han Qiu and Cheng Zhang and Zongming Fei and Meikang Qiu and Sun-Yuan Kung",

booktitle = "Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings",

address = "Germany",

}

Zhao, H, Su, P, Wei, Y, Gai, K & Qiu, M 2021, GAN-Enabled Code Embedding for Reentrant Vulnerabilities Detection. 在 H Qiu, C Zhang, Z Fei, M Qiu & S-Y Kung (编辑), 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), 卷 12817 LNAI, Springer Science and Business Media Deutschland GmbH, 页码 585-597, 14th International Conference on Knowledge Science, Engineering and Management, KSEM 2021, Tokyo, 日本, 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 等.
Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings. 编辑 / Han Qiu; Cheng Zhang; Zongming Fei; Meikang Qiu; Sun-Yuan Kung. Springer Science and Business Media Deutschland GmbH, 2021. 页码 585-597 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 12817 LNAI).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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

UR - http://www.scopus.com/inward/record.url?scp=85113741097&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-82153-1_48

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

A2 - Qiu, Han

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. 在 Qiu H, Zhang C, Fei Z, Qiu M, Kung SY, 编辑, Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. 页码 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

摘要

出版系列

会议

访问文件

其它文件与链接

指纹

引用此