陈红松,陈京九.基于ResNet和双向LSTM融合的物联网入侵 检测分类模型构建与优化研究[J].湖南大学学报:自然科学版,2020,(8):1~8
基于ResNet和双向LSTM融合的物联网入侵 检测分类模型构建与优化研究
Study on Construction of IOT Network Intrusion Detection Classification Model and Optimization Based on Combination of ResNet and Bidirectional LSTM Network
  
DOI:
中文关键词:  入侵检测  残差网络  双向LSTM网络  图像分类  物联网
英文关键词:intrusion detection  Residual Networks(ResNet)  bidirectional Long-Short Term Memory(LSTM) networks  image classification  IOT(Internet of Things)
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作者单位
陈红松,陈京九 (北京科技大学 计算机与通信工程学院北京 100083) 
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中文摘要:
      为提高物联网入侵检测模型的综合性能,将残差神经网络(Residual Networks,ResNet)与双向长短时记忆(Long-Short Term Memory,LSTM)网络融合,构建物联网入侵检测分类模型.针对大规模物联网流量快速批量处理问题,在对原始数据进行清洗、转换等预处理基础上,提出将多条流量样本转换为灰度图,并利用基于ResNet和双向LSTM融合的深度学习方法构建物联网入侵检测分类模型.对分类模型的网络结构、可复用性进行综合优化实验,得到最终优化模型,分类准确率达到96.77%,综合优化后的模型构建时间为39.85 s.与其他机器学习算法结果相比,该优化方法在分类准确率和效率两个方面取得了很好的效果,综合性能优于传统的入侵检测分类模型.
英文摘要:
      In order to improve the performance of the Internet of Things (IOT) network intrusion detection model, Residual Networks (ResNet) and bidirectional Long-Short Term Memory (LSTM) networks were combined,and an IOT intrusion detection classification model was constructed. For the rapid and batch processing problem of large-scale IOT traffic, multiple traffic samples were converted into grayscale images. Then,the grayscale images were used to construct IOT intrusion detection and classification model which combined with ResNet and bidirectional LSTM network. The network structure and re-usability of the classification model were optimized experimentally,so the optimization model was obtained finally. The classification accuracy of the optimization model is 96.77%, and the running time after the model reuse optimization is 39.85 s. Compared with other machine learning algorithms, the proposed approach achieves good results in both classification accuracy and efficiency. The performance of the proposed model is better than that of traditional intrusion detection model.
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