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1426 软件学报 2026 年第 37 卷第 3 期
Intelligence. AAAI, 2021. 8410–8418. [doi: 10.1609/aaai.v35i9.17022]
[30] Cheng Z, Zhu F, Zhang XY, Liu CL. Adversarial training with distribution normalization and margin balance. Pattern Recognition, 2023,
136: 109182. [doi: 10.1016/j.patcog.2022.109182]
[31] Zhao K, Kang QY, Song Y, She R, Wang SJ, Tay WP. Adversarial robustness in graph neural networks: A Hamiltonian approach. In:
Proc. of the 37th Int’l Conf. on Neural Information Processing Systems. New Orleans: Curran Associates Inc., 2024. 148.
[32] Han Y, Ji TT, Wang ZR, Liu H, Jiang H, Wang WD, Cui X. An adversarial smart contract honeypot in Ethereum. Computer Modeling in
Engineering & Sciences, 2021, 128(1): 247–267. [doi: 10.32604/cmes.2021.015809]
[33] Ding YX, Wei D, Zhang YB, Xue CL. Malicious code detection using opcode running tree representation. In: Proc. of the 9th Int’l Conf.
on P2P, Parallel, Grid, Cloud and Internet Computing. Guangzhou: IEEE, 2014. 616–621. [doi: 10.1109/3PGCIC.2014.140]
[34] Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv:1301.3781, 2013.
[35] Pennington J, Socher R, Manning CD. GloVe: Global vectors for word representation. In: Proc. of the 2014 Conf. on Empirical Methods
in Natural Language Processing (EMNLP). Doha: ACL, 2014. 1532–1543. [doi: 10.3115/v1/D14-1162]
[36] Chen YH. Convolutional neural network for sentence classification [MS. Thesis]. Waterloo: University of Waterloo, 2015.
[37] Chen TQ, Guestrin C. XGBoost: A scalable tree boosting system. In: Proc. of the 22nd ACM SIGKDD Int’l Conf. on Knowledge
Discovery and Data Mining. San Francisco: ACM, 2016. 785–794. [doi: 10.1145/2939672.2939785]
[38] Ke GL, Meng Q, Finley T, Wang TF, Chen W, Ma WD, Ye QW, Liu TY. LightGBM: A highly efficient gradient boosting decision tree.
In: Proc. of the 31st Int’l Conf. on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017. 3149–3157.
[39] Breiman L. Random forests. Machine Learning, 2001, 45(1): 5–32. [doi: 10.1023/A:1010933404324]
[40] LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc. of the IEEE, 1998, 86(11):
2278–2324. [doi: 10.1109/5.726791]
作者简介
宁小勇, 硕士生, 主要研究领域为机器学习, 以太坊庞氏骗局智能合约检测, 网络安全.
高睿杰, 硕士生, 主要研究领域为网络安全, 以太坊钓鱼行为研究.
叶楚涵, 硕士生, 主要研究领域为人工智能, 以太坊勒索行为检测.
刘园, 博士, 教授, 博士生导师, 主要研究领域为网络安全威胁情报共享, 数据安全, 区块链安全.
王兴伟, 博士, 教授, 博士生导师, CCF 会士, 主要研究领域为互联网, 云计算, 网络安全.
黄敏, 博士, 教授, 博士生导师, 主要研究领域为数据解析与机器学习, 计算机智能软件工程.

