Smart Contract Vulnerability Detection Method Based on Capsule Network and Attention Mechanism

doi:10.12141/j.issn.1000-565X.220167

Abstract

Abstract:

In recent years, with the increasing number of smart contracts and the increasing economic losses caused by contract loopholes, the security of smart contracts has attracted more and more attention. The vulnerability detection method based on deep learning can solve the problems of low detection efficiency and insufficient accuracy of the early traditional smart contract vulnerability detection method. However, most of the existing deep learning-based vulnerability detection methods directly use smart contract source code, opcode sequence or bytecode sequence as the input of the deep learning model. This fact will weaken the effective information due to the introduction of too much invalid information. To solve this problem, this paper proposed a smart contract vulnerability detection method based on capsule network and attention mechanism. Considering the execution timing information of the program, the study extracted key operation code sequence of the smart contract as the source code feature. Then a hybrid network structure of capsule network and attention mechanism was used for training. The capsule network extracts the context information of the smart contract and the connection between the part and the whole; while the attention mechanism is used to assign different weights to different opcodes according to their importance. The experimental results show that the F₁ score and accuracy of the algorithm proposed in this paper in the smart contract data set are 94.48% and 97.15%, indicating that this algorithm is superior to other detection methods in performance.

Key words: smart contract, key opcode sequence, capsule network, attention mechanism

CLC Number:

TP391

LU Lu, LAI Jinxiong. Smart Contract Vulnerability Detection Method Based on Capsule Network and Attention Mechanism[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(5): 36-44.

Figures/Tables 7

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实验方法	r_p	r_r	F₁	r_a
Mythril	1.000 0	0.561 6	0.719 2	0.896 0
SmartCheck	1.000 0	0.428 6	0.600 0	0.852 4
Oyente	1.000 0	0.458 0	0.628 3	0.886 3
Securify	1.000 0	0.567 8	0.724 3	0.915 6
SmartEmbed	0.831 4	0.652 3	0.731 0	0.814 6
TMP	0.881 5	0.923 5	0.902 1	0.935 6
AWD-LSTM	0.901 4	0.910 3	0.905 8	0.910 0
CapsNet-Att	0.954 2	0.935 6	0.944 8	0.971 5

实验方法	检测耗时/s	参数量/10⁶
Oyente	8.36
SmartCheck	5.24
Mythril	49.26
Securify	105.29
SmartEmbed	0.52
TMP	0.25	7.45
AWD-LSTM	0.37	9.87
CapsNet-Att	0.15	5.46

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