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Smart Contract Vulnerability Detection Model Based on Siamese Network

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Smart Computing and Communication (SmartCom 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13828))

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Abstract

Blockchain is experiencing the transition from the first generation to the second generation, and smart contract is the symbol of the second generation blockchain. Under the background of the explosive growth of the second-generation blockchain platform and applications represented by smart contracts, frequent smart contract vulnerability events seriously threaten the ecological security of the blockchain, reflecting the importance and urgency of smart contract vulnerability detection. In this paper, we proposed a smart contract vulnerability detection method based on a Siamese network. We combined the Siamese network with Long Short-Term Memory (LSTM) Network neural network to complete the task of smart contract vulnerability detection. The Siamese network used in this paper consists of two subnetworks that share the same parameters onto a low dimension and easily separable feature space. Siamese network is now widely used in the field of image similarity and target tracking. In this paper, we improve the Siamese network so that it can be used for smart contract vulnerability detection. By comparing with previous research results, the model has better vulnerability detection performance and a lower false-positive rate.

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References

  1. Giancaspro, M.: Is a ‘smart contract’ really a smart idea? Insights from a legal perspective. Comput. Law Secur. Rev. 33(6). 825–835(2017)

    Google Scholar 

  2. Qiu, H., Qiu, M., Memmi, G.: A dynamic scalable blockchain based communication architecture for IoT. In: International Conference on Smart Blockchain, pp. 159–166 (2018)

    Google Scholar 

  3. Khan, S.N., Loukil, F., Ghedira-Guegan, C., Benkhelifa, E., Bani-Hani, A.: Blockchain smart contracts: applications, challenges, and future trends. Peer-to-Peer Networking Appl. 14(5), 2901–2925 (2021). https://doi.org/10.1007/s12083-021-01127-0

    Article  Google Scholar 

  4. Oliva, G.A., Hassan, A.E., Jiang, Z.M.: An exploratory study of smart contracts in the Ethereum blockchain platform. Empir. Softw. Eng. 25(3), 1864–1904 (2020). https://doi.org/10.1007/s10664-019-09796-5

    Article  Google Scholar 

  5. Samreen, N.F., Alalfi, M.H.: Reentrancy vulnerability identification in ethereum smart contracts. In: 2020 IEEE International Workshop on Blockchain Oriented Software Engineering (IWBOSE), pp. 22–29 (2020)

    Google Scholar 

  6. Karevan, Z., Suykens,A. Transductive lstm for time-series prediction: An application to weather forecasting. Neural Networks 125, 1–9 (2020)

    Google Scholar 

  7. Wu, H., et al.: Peculiar: Smart contract vulnerability detection based on crucial data flow graph and pre-training techniques. In: 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE), pp. 378–389 (2021)

    Google Scholar 

  8. Ghaleb, A., Pattabiraman, K.: How effective are smart contract analysis tools? evaluating smart contract static analysis tools using bug injection. In: Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, pp. 415–427 (2020)

    Google Scholar 

  9. Yu, X., Zhao, H. Hou, B., Ying, Z., Wu, B. Deescvhunter: a deep learning-based framework for smart contract vulnerability detection. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2021)

    Google Scholar 

  10. Qian, P., Liu, Z., He, Q., Zimmermann, R., Wang X. Towards automated reentrancy detection for smart contracts based on sequential models. IEEE Access 8, 119685–19695(2020)

    Google Scholar 

  11. Zhuang, Y., Liu, Z., Qian, P., Liu, Q., Wang, X., He, Q.: Smart contract vulnerability detection using graph neural network. In: IJCAI 2020, pp. 3283–3290 (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Information Engineering, Yangzhou University, Yangzhou, 225127, China

    Weijie Chen & Lejun Zhang

  2. Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, 510006, China

    Ran Guo, Lejun Zhang, Jing Qiu, Shen Su, Yuan Liu & Guangxia Xu

  3. Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education, Beijing, 100039, China

    Guopeng Wang

  4. Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China

    Huiling Chen

Authors
  1. Weijie Chen
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  2. Ran Guo
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  3. Guopeng Wang
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  4. Lejun Zhang
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  5. Jing Qiu
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  6. Shen Su
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  7. Yuan Liu
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  8. Guangxia Xu
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  9. Huiling Chen
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Corresponding authors

Correspondence to Ran Guo , Guopeng Wang or Lejun Zhang .

Editor information

Editors and Affiliations

  1. Dakota State University, Madison, SD, USA

    Meikang Qiu

  2. Fudan University, Shanghai, China

    Zhihui Lu

  3. Ibaraki University, Ibaraki, Japan

    Cheng Zhang

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

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Cite this paper

Chen, W. et al. (2023). Smart Contract Vulnerability Detection Model Based on Siamese Network. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_60

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  • DOI: https://doi.org/10.1007/978-3-031-28124-2_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28123-5

  • Online ISBN: 978-3-031-28124-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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