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Alternative TitleResearch on GAN-SDAE-RF Model for Network Intrusion Detection
安磊1; 韩忠华1,2; 林硕1; 尚文利3,4,5
Source Publication计算机工程与应用
Contribution Rank2
Funding Organization国家自然科学基金面上项目(No.61773368) ; 辽宁省教育厅青年科技人才“育苗”项目(No.Inqn201912) ; 沈阳市科技计划双百工程项目(No.Z18-5-015)
Keyword深度学习 生成式对抗网络 栈式降噪自编码器 随机森林算法


Other Abstract

Aiming at the problem of low detection rate of rare attacks in traditional machine learning methods when dealing with unbalanced massive high-dimensional data, an intrusion detection model based on deep learning and random forest algorithm is proposed. In order to avoid the problems of low classification accuracy, poor stability and low detection rate of rare attacks when traditional random forests face high-dimensional data and unbalanced data, Generative Adversarial Network and Stacked Denoising Autoencoder were introduced into the Random Forest algorithm for improvement. First, the rare attack data set is input into the GAN neural network to generate a new attack sample to improve the uneven distribution of network intrusion data in the sample set. Then, the deep-stacked SDAE extracts the distribution rules of the network data layer by layer, and combines the coefficient penalty and reconstruction error of each coding layer to determine the features related to the intrusion behavior in the high-dimensional data. The forest decision tree is constructed based on the characteristic data after dimension reduction. The experimental results using the UNSW-NB15 data set show that compared with SVM, KNN, CNN, LSTM, and DBN methods, the overall detection accuracy of GAN-SDAE-RF has increased by 9.39% on average, and the FPR and FNR have decreased by 9% and 15.24% on average. The detection rates on Shellcode, Backdoor, and Worms have increased by 26.8%, 27.98%, 27.85%, and 39.97% respectively.

Document Type期刊论文
Corresponding Author安磊
Recommended Citation
GB/T 7714
安磊,韩忠华,林硕,等. 面向网络入侵检测的GAN-SDAE-RF模型研究[J]. 计算机工程与应用,2020:1-13.
APA 安磊,韩忠华,林硕,&尚文利.(2020).面向网络入侵检测的GAN-SDAE-RF模型研究.计算机工程与应用,1-13.
MLA 安磊,et al."面向网络入侵检测的GAN-SDAE-RF模型研究".计算机工程与应用 (2020):1-13.
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