梁欣怡,行鸿彦,侯天浩.基于自监督特征增强的 CNN-BiLSTM
网络入侵检测方法[J].电子测量与仪器学报,2022,36(10):65-73 |
基于自监督特征增强的 CNN-BiLSTM
网络入侵检测方法 |
CNN-BiLSTM network intrusion detection method based onself-supervised feature enhancement |
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DOI: |
中文关键词: 深度学习 自监督学习 数据增强 网络入侵检测 |
英文关键词:deep learning self-supervised learning data enhancement network intrusion detection |
基金项目:国家重点研发计划(2021YFE0105500)、国家自然科学基金(62171228)项目资助 |
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中文摘要: |
针对网络入侵检测中攻击样本和流量特征不足的问题,提出一种基于自监督特征增强的 CNN-BiLSTM 网络入侵检测方
法,实现在流量数据中检测异常网络流量的目标。 通过分析流量特征数据分布差异,采用 IQR 异常值处理方法进行数据预处
理,使用自编码器对攻击样本进行数据增强,构建 CNN-BiLSTM 神经网络和自编码器组成半自监督模型,分别提取高维流量特
征和自监督特征,将组合特征作为最终特征输入到分类模型中进行预测分类,实现网络入侵检测。 实验结果表明,与其他入侵
检测方法相比,所提方法在准确率和 F1 分数上分别达到了 85. 7%和 85. 1%,能够有效提高网络入侵的检测精度以及对未知攻
击的检测能力。 |
英文摘要: |
Aiming at the problem of insufficient attack samples and traffic characteristics in network intrusion detection, a CNN-BiLSTM
network intrusion detection method based on self-supervised feature enhancement was proposed to detect abnormal network traffic in
traffic data. By analyzing the difference in the distribution of traffic characteristic, IQR outlier processing method was used for data
preprocessing, and autoencoder was used to enhance the number of attack samples. A semi-self-supervised model composed of CNNBilSTM neural network and autoencoder was constructed to extract high-dimensional traffic characteristics and self-supervised features
respectively. The combined features are input into the classification model as the final features for prediction and classification, so as to
realize the function of network intrusion detection. The experimental results show that compared with other intrusion detection methods,
the accuracy and F1 score of the proposed method are 85. 7% and 85. 1% respectively, which can effectively improve the detection
accuracy of network intrusion and the detection ability of unknown attacks. |
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