西南交通大学学报2023，Vol.58Issue(6)：1277-1285,9.DOI:10.3969/j.issn.0258-2724.20210557
小样本条件下列车通信网络攻击样本生成方法
Attack-Sample Generation Method for Train Communication Network Under Few-Shot Condition
摘要
Abstract
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 F1 score increase by 4.23%,which means that the detection capability is effectively improved.关键词
列车通信网络/入侵检测/深度学习/样本生成/生成对抗网络Key words
train communication network/intrusion detection/deep learning/sample generation/generative adversarial network分类
交通运输引用本文复制引用
岳川,王立德,闫海鹏..小样本条件下列车通信网络攻击样本生成方法[J].西南交通大学学报,2023,58(6):1277-1285,9.基金项目
中国国家铁路集团有限公司科技研究开发计划(N2020J007) （N2020J007）
