SIA OpenIR  > 工业控制网络与系统研究室
结合栈式自编码及长短时记忆的入侵检测研究
Alternative TitleResearch on Intrusion Detection Based on Stacked Autoencoder and Long-short Memory
林硕1; 安磊1; 高治军1; 单丹1; 尚文利2,3,4
Department工业控制网络与系统研究室
Source Publication系统仿真学报
ISSN1004-731X
2020
Pages1-9
Contribution Rank2
Funding Organization国家自然科学基金面上项目(61773368) ; 辽宁省教育厅科学技术项目(Injc201912) ; 辽宁省教育厅青年科技人才“育苗”项目(Inqn201912)
Keyword深度学习 入侵检测技术 栈式降噪自编码器 长短时记忆网络
Abstract

目前,针对网络攻击越来越隐蔽,且具有智能化和复杂化的特点,浅层的机器学习已经无法及时应对,文中提出了一种基于SDAE(Stacked Denoising Autoencoder)和LSTM(Long Short-Term Memory)相结合的深度学习方法。首先,通过堆叠深层的SDAE智能逐层抽取网络数据的分布规则,结合各个编码层的系数惩罚和重构误差对高维数据进行多样性异常特征提取。然后,结合LSTM的记忆功能和强大的序列数据学习能力进行学习分类。最后,在UNSW-NB15数据集上进行了实验,通过调整时间步长进行分析,实验结果表明,该模型具有检测准确率高、误报率低的优点。

Other Abstract

At present, in view of the increasingly hidden, intelligent and complex characteristics of network attacks, shallow machine learning is no longer timely. In this paper, A deep learning method based on the combination of SDAE and LSTM is proposed. Firstly, the distribution rules of network data were extracted layer by layer by intelligent stacking of SDAE deep layer, and the diversity anomaly features of high-dimensional data were extracted by combining coefficient penalty and reconstruction error of each coding layer. Then, combine the memory function of LSTM and the powerful sequence data learning ability to classify learning. Finally, the experiments were carried out on the UNSW-NB15 data set, and the analysis was performed by adjusting the time step. The experimental results show that the model has the advantages of high detection accuracy and low false alarm rate.

Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26922
Collection工业控制网络与系统研究室
Corresponding Author高治军
Affiliation1.沈阳建筑大学信息与控制工程学院
2.中国科学院沈阳自动化研究所工业控制网络与系统研究室
3.中国科学院网络化控制系统重点实验室
4.中国科学院机器人与智能制造创新研究院
Recommended Citation
GB/T 7714
林硕,安磊,高治军,等. 结合栈式自编码及长短时记忆的入侵检测研究[J]. 系统仿真学报,2020:1-9.
APA 林硕,安磊,高治军,单丹,&尚文利.(2020).结合栈式自编码及长短时记忆的入侵检测研究.系统仿真学报,1-9.
MLA 林硕,et al."结合栈式自编码及长短时记忆的入侵检测研究".系统仿真学报 (2020):1-9.
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