Abstract
In recent years, the second-generation blockchain platforms and applications represented by smart contracts have seen explosive growth, but frequent smart contract vulnerability incidents have seriously threatened the ecological security of blockchain. In view of the low efficiency of code audit based on expert experience, it is important to develop a general automation tool to mine smart contract vulnerabilities. In the beginning, the security threats of smart contracts should be investigated and analyzed, and many smart contract vulnerabilities and attack modes that occur frequently, such as code reentrant, access control, integer overflow, etc., were summarized. Then, the technology method of smart contract vulnerability detection conforming to The Times is obtained, and the current samples of smart contract vulnerability detection are summarized. The current investigation includes too few types of vulnerabilities, with a variety of inaccuracies and deviations. It is only carried out through manual audit. Through these methods, according to the state of including after put forward the general ideas of this kind of situation, and puts forward a kind of symbolic execution auxiliary fuzzy test framework, can reduce the symbolic execution channel congestion and fuzzy test code coverage degree is too little, so as to improve test efficiency, easy to dig holes of large and medium-sized intelligent contracts quality improvement.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ding M, Li P, Li S et al (2021) HFContractFuzzer: fuzzing hyperledger fabric smart contracts for vulnerability detection
Fu M, Wu L, Hong Z et al (2019) A critical-path-coverage-based vulnerability detection method for smart contracts. IEEE Access 99:1–1
Ye J, Ma M, Peng T et al, A software analysis based vulnerability detection system for smart contracts. The University of Science and Technology of China, Hefei, China
Masi M (2019) Contract fuzzer: fuzzing smart contracts for vulnerability detection. Comput Rev 60(12):467–468
Ashizawa N, Yanai N, Cruz JP et al (2021) Eth2Vec: learning contract-wide code representations for vulnerability detection on ethereum smart contracts
Huang Y, Jiang B, Chan WK (2020) EOS fuzzer: fuzzing EOSIO smart contracts for vulnerability detection. IEEE
Wang D, Jiang B, Chan WK (2020) WANA: Symbolic execution of wasm bytecode for cross-platform smart contract vulnerability detection
Xu X, Chang L, Qian F et al (2017) Neural network-based graph embedding for cross-platform binary code similarity detection
Song J, He H, Lv Z et al (2019) An efficient vulnerability detection model for ethereum smart contracts
Lutz O, Chen H, Fereidooni H et al (2021) ESCORT: ethereum smart contracts vulnerability detection using deep neural network and transfer learning
Kim D-H, Ha J-E et al (2017) Multi-lane detection using convolutional neural networks and transfer learning. J Inst Control Robot Syst
Menglin FU, Lifa WU, Hong Z et al (2019) Research on vulnerability mining technique for smart contracts. J Comput Appl
Liu Z, Qian P, Wang X et al (2021) Smart contract vulnerability detection: from pure neural network to interpretable graph feature and expert pattern fusion
Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (62162020), the Science Project of Hainan University (KYQD(ZR)20021).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jia, D., Liu, Z., Zhang, Z., Ye, J. (2022). Research on Security Vulnerability Detection of Smart Contract. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City. Lecture Notes on Data Engineering and Communications Technologies, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-16-7469-3_108
Download citation
DOI: https://doi.org/10.1007/978-981-16-7469-3_108
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7468-6
Online ISBN: 978-981-16-7469-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)