MFF-SC: A multi-feature fusion method for smart contract classification

Abstract

The classification of the smart contract can effectively reduce the search space and improve retrieval efficiency. The existing classification methods are based on natural language processing technologies. Because the processing of source code by these technologies lacks extraction and processing in the software engineering field, there is still a lot of room for improvement in their methods of feature extraction. Therefore, this paper proposes a multi-feature fusion method for smart contract classification (MFF-SC) based on the code processing technology. From the source code perspective, source code processing method and attention mechanism are used to extract local code features. Structure-based traversal method are used to extract global code features from abstract syntax tree. Local and global code features introduce attention mechanism to generate code semantic features. From the perspective of account transaction, the feature of account transaction is extracted by using TransR. Next, the code semantic features and account transaction features generate smart contract semantic features by an attention mechanism. Finally, the smart contract semantic features are fed into a stacked denoising autoencoder and a softmax classifier for classification. Experimental results on a real dataset show that MFF-SC achieves an accuracy rate of 83.9%, compared with other baselines and variants.

Keywords

Smart contract classification abstract syntax tree feature extraction feature representation feature fusion

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References

Luu

Chu

D.-H.

Olickel

Saxena

and Hobor

, Making smart contracts smarter, in: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016, pp. 254–269.

Mohanta

B.K.

Panda

S.S.

and Jena

, An overview of smart contract and use cases in blockchain technology, in: 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 2018, pp. 1–4.

Allamanis

Barr

E.T.

Devanbu

and Sutton

, A survey of machine learning for big code and naturalness, ACM Computing Surveys (CSUR) 51(4) (2018), 1–37.

Badari

and Chaudhury

, An Overview of Bitcoin and Ethereum White-Papers, Forks and Prices, April 26, 2021.

Huang

Liu

and Chen

, Towards automatic smart-contract codes classification by means of word embedding model and transaction information, Acta Automatica Sinica 43(9) (2017), 1532–1543.

Yuxin

Ting

and Dabin

, Automatic smart contract classification model based on hierarchical attention mechanism and bidirectional long short-term memory neural network, Journal of Computer Applications 40(4) (2020), 978.

Tian

Wang

Zhao

Guo

Sun

and Lv

, Smart contract classification with a bi-LSTM based approach, IEEE Access 8 (2020), 43806–43816.

Jiang

Chen

Wen

and Zheng

, Applying blockchain-based method to smart contract classification for CPS applications, Digital Communications and Networks, 2022.

Shi

Xiang

Gao

Sood

and Doss

R.R.M.

, A bytecode-based approach for smart contract classificatio, in: 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE, 2022, pp. 1046–1054.

10.

Ortin

Rodriguez-Prieto

Pascual

and Garcia

, Heterogeneous tree structure classification to label Java programmers according to their expertise level, Future Generation Computer Systems 105 (2020), 380–394.

11.

Xia

and Jin

, Deep code comment generation, in: 2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC), IEEE, 2018, pp. 200–20010.

12.

Lin

Liu

Sun

Liu

and Zhu

, Learning entity and relation embeddings for knowledge graph completion, in: Twenty-ninth AAAI Conference on Artificial Intelligence, 2015.

13.

Parr

and Quong

, ANTLR: A predicatedâ€LL (k) parser generator, Software: Practice and Experience 25(7) (1995), 789–810.

14.

Srivastava

Hinton

Krizhevsky

Sutskever

and Salakhutdinov

, Dropout: A simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research 15(1) (2014), 1929–1958.

15.

Mikolov

Chen

Corrado

and Dean

, Efficient estimation of word representations in vector space, arXiv preprint, arXiv:1301.3781, 2013.

16.

Vincent

Larochelle

Bengio

and Manzagol

P.A.

, Extracting and composing robust features with denoising autoencoders, in: Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 1096–1103.

17.

Vincent

Larochelle

Lajoie

Bengio

Manzagol

P.A.

and Bottou

, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of Machine Learning Research 11(12) (2010).

18.

Morstatter

Nazer

T.H.

Carley

K.M.

and Liu

, A new approach to bot detection: striking the balance between precision and recall, in: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE, 2016, pp. 533–540.

19.

Stoica

and Wang

, On robust Capon beamforming and diagonal loading, IEEE Transactions on Signal Processing 51(7) (2003), 1702–1715.

20.

Jang

Kim

Harerimana

Kang

S.U.

and Kim

J.W.

, Bi-LSTM model to increase accuracy in text classification: Combining Word2vec CNN and attention mechanism, Applied Sciences 10(17) (2020), 5841.

21.

Song

and Yan

, Chinese text emotion classification model based on CNN-BIGRU, Comput. Technol. Dev 30(5) (2020).

22.

Zhang

Wang

Zhang

Sun

Wang

and Liu

, A novel neural source code representation based on abstract syntax tree, in: 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), IEEE, 2019, pp. 783–794.

23.

Alon

Zilberstein

Levy

and Yahav

, code2vec: Learning distributed representations of code, Proceedings of the ACM on Programming Languages 3(POPL) (2019), 1–29.

24.

Chen

Dai

Xie

and Tan

, Improving Ponzi Scheme Contract Detection Using Multi-Channel TextCNN and Transformer, Sensors 21(19) (2021), 6417.

25.

Yang

Keung

Wei

and Zhang

, A multi-modal transformer-based code summarization approach for smart contracts, in: 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC), IEEE, 2021, pp. 1–12.

26.

LeClair

Jiang

and McMillan

, A neural model for generating natural language summaries of program subroutines, in: 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), IEEE, 2019, pp. 795–806.

27.

Wei

Xia

and Jin

, Code generation as a dual task of code summarization, Advances in Neural Information Processing Systems 32 (2019).

28.

Xia

and Jin

, Deep code comment generation with hybrid lexical and syntactical information, Empirical Software Engineering 25(3) (2020), 2179–2217.

29.

Zhang

Yuan

N.J.

Lian

Xie

and Ma

W.-Y.

, Collaborative knowledge base embedding for recommender systems, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 353–362.

30.

and Altenbek

, A recommendation algorithm based on fine-grained feature analysis, Expert Systems with Applications 163 (2021), 113759.

31.

Kiela

Wang

and Cho

, Dynamic meta-embeddings for improved sentence representations, arXiv preprint, arXiv:1804.07983, 2018.

32.

Maharjan

Montes

Gonzalez

F.A.

and Solorio

, A genre-aware attention model to improve the likability prediction of books, in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018, pp. 3381–3391.

33.

Yang

Dyer

Smola

and Hovy

, Hierarchical attention networks for document classification, in: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016, pp. 1480–1489.

34.

Vaswani

Shazeer

Parmar

Uszkoreit

Jones

Gomez

A.N.

Kaiser

and Polosukhin

, Attention is all you need, Advances in Neural Information Processing Systems 30 (2017).

35.

and Feng

, Application of stacked denoising autoencoder in spamming filtering, Journal of Computer Applications 35(11) (2015), 3256–3260.

36.

Wang

, Sentiment Classification and Parallelization Research Based on Stacked Denoising Autoencoders, Telecommunications Technology 12 (2017), 8–12.

37.

Jiang

Zhang

and Wang

, Hierarchy construction and classification of heterogeneous information networks based on stacked denoising auto encoder, Journal of Beijing University of Technology 44(9) (2018), 1217–1226.