- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | YUE Chuan, WANG Lide, YAN Haipeng. Attack-Sample Generation Method for Train Communication Network Under Few-Shot Condition[J]. Journal of Southwest Jiaotong University, 2023, 58(6): 1277-1285. doi: 10.3969/j.issn.0258-2724.20210557
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Deep learning-based intrusion detection for the train communication network requires sufficient training samples, but there are few available attack samples in the actual scenario. Generative adversarial network (GAN) thus operates to generate attack samples. Also, the sampling strategy, constraint condition and loss function of GAN are improved; and a generator based on convolutional neural network and a discriminator are designed. Then an improved GAN-based method is proposed for attack sample generation. Sample generation experiments and intrusion detection experiments are conducted to test the proposed method, indicating that it can generate effective attack samples. When applying these generated samples in the training process of the intrusion detection model, the average
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