基于字节码的以太坊智能合约分类方法

doi:10.11959/j.issn.2096-109x.2022046

Abstract

Abstract:

In recent years, blockchain technology has been widely used and concerned in many fields, including finance, medical care and government affairs.However, due to the immutability of smart contracts and the particularity of the operating environment, various security issues occur frequently.On the one hand, the code security problems of contract developers when writing contracts, on the other hand, there are many high-risk smart contracts in Ethereum, and ordinary users are easily attracted by the high returns provided by high-risk contracts, but they have no way to know the risks of the contracts.However, the research on smart contract security mainly focuses on code security, and there is relatively little research on the identification of contract functions.If the smart contract function can be accurately classified, it will help people better understand the behavior of smart contracts, while ensuring the ecological security of smart contracts and reducing or recovering user losses.Existing smart contract classification methods often rely on the analysis of the source code of smart contracts, but contracts released on Ethereum only mandate the deployment of bytecode, and only a very small number of contracts publish their source code.Therefore, an Ethereum smart contract classification method based on bytecode was proposed.Collect the Ethereum smart contract bytecode and the corresponding category label, and then extract the opcode frequency characteristics and control flow graph characteristics.The characteristic importance is analyzed experimentally to obtain the appropriate graph vector dimension and optimal classification model, and finally the multi-classification task of smart contract in five categories of exchange, finance, gambling, game and high risk is experimentally verified, and the F1 score of the XGBoost classifier reaches 0.913 8.Experimental results show that the algorithm can better complete the classification task of Ethereum smart contracts, and can be applied to the prediction of smart contract categories in reality.

Key words: blockchain, smart contract, bytecode, classification

CLC Number:

TP393

Dan LIN, Kaixin LIN, Jiajing WU, Zibin ZHENG. Bytecode-based approach for Ethereum smart contract classification[J]. Chinese Journal of Network and Information Security, 2022, 8(5): 111-120.

Figures/Tables 12

References 26

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操作码值	描述	举例
0x00～0x0B	停止和算术操作	STOP,ADD,SUB,DIV
0x10～0x1A	比较和按位逻辑操作	LT,GT,EQ,ISZERO
0x20	加密操作	SHA3
0x30～0x3E	环境信息	ADDRESS,CALLER
0x40～0x45	块信息	BLOCKHASH,COINBASE
0x50～0x5B	堆栈、内存、存储和流操作	POP,JUMP,JUMPI,JUMPDEST
0x60～0x7F	Push 操作	PUSH1～PUSH32
0x80～0x8F	复制操作	DUP1～DUP16
0x90～0x9F	交换操作	SWAP1～SWAP16
0xA0～0xA4	日志操作	LOG0-LOG4
0xF0～0xFF	系统操作	CALL,RETURN

合约类别	合约数量
交易所	2 493
金融	182
赌博	1 020
游戏	877
高风险	185

智能合约地址	创建者地址	运行字节码
0xc66ea802717b	0x12bcc9daff	0x606060405236156100c1576
fb9833400264dd	da452b6c4b0	000357c010000000000000000
12c2bceaa34a6d	a1571360925	0000000000000000000000000
	a64fcc79	0000000000000009004806309
		5ea7b3146100c357806318160d
		dd146100f857806323b872dd14
		61011b57806323de6651146101
		595780633018205f1461018357
		80635687f2b8…

类别	合约地址	x_0	x_1	...	MLOAD	SWAP2
金融	0x00000000219ab540356cbb839cbe05303d7705fa	0.44018072	0.53969365	...	158	152
交易所	0x0000000f8ef4be2b7aed6724e893c1b674b9682d	0.19015524	0.37818763	...	206	150
交易所	0x000005edbbc1f258302add96b5e20d3442e5dd89	0.27618885	0.3796904	...	214	157

分类模型	参数
决策树	Criterion = ‘entropy’, max_depth = 10
随机森林	max_depth = 10
朴素贝叶斯	均为sklearn.naive_bayes.GaussianNB的默认值
XGBoost	learning_rate = 0.30, objective = ‘multi:softprob’, tree_method = ‘exact’
SVM	decision_function_shape = ‘ovo’
GBDT	均为 sklearn.ensemble.GradientBoostingClassifier 的默认值

Bytecode-based approach for Ethereum smart contract classification

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分类模型	精确率	召回率	F1值
朴素贝叶斯（高斯分布）	0.846 0	0.744 4	0.769 2
SVM	0.900 3	0.874 6	0.873 0
决策树	0.879 6	0.876 1	0.877 3
随机森林	0.917 4	0.894 3	0.896 0
GBDT	0.910 5	0.902 7	0.903 8
XGBoost	0.917 6	0.911 1	0.911 1

维度	精确率	召回率	F1值
16维	0.746 4	0.743 7	0.743 0
32维	0.856 4	0.847 3	0.844 7
64维	0.847 2	0.838 9	0.836 3
128维	0.849 5	0.843 8	0.840 1

采用特征	精确率召回率	F1值
操作码频率特征	0.926 30.920 2	0.921 1
控制流图特征	0.685 90.690 8	0.668 8
操作码频率特征+控制流图特征	0.917 60.911 1	0.911 1

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类别	精确率	召回率	F1值	数据量
交易所	0.994 6	0.954 6	0.974 2	771
金融	0.705 9	0.734 7	0.720 0	49
赌博	0.937 7	0.892 0	0.914 3	287
游戏	0.785 5	0.950 4	0.860 1	262
高风险	0.787 2	0.627 1	0.698 1	59
总计	0.926 3	0.920 2	0.921 1	1 428