Network intrusion detection model based on multivariate correlation analysis - long short-time memory network | |
Dong, Rui-Hong; Li, Xue-Yong; Zhang, Qiu-Yu; Yuan, Hui | |
刊名 | IET INFORMATION SECURITY |
2020-03 | |
卷号 | 14期号:2页码:166-174 |
关键词 | support vector machines neural nets image classification pattern classification security of data recurrent neural nets learning (artificial intelligence) network intrusion detection model multivariate correlation analysis short-time memory network low detection performance time correlation characteristics intrusion detection datasets multivariate correlations analysis short-term memory network information gain feature selection method convolutional neural networks recurrent neural network classification detection performance |
ISSN号 | 1751-8709 |
DOI | 10.1049/iet-ifs.2019.0294 |
英文摘要 | For the purpose of improving the low detection performance of network intrusion detection model caused by high-dimensional data, and from the perspective of time correlation characteristics of intrusion detection datasets, the authors present a network intrusion detection model based on the multivariate correlations analysis - long short-term memory network (MCA-LSTM). Firstly, this model selects the optimal feature subsets through the information gain feature selection method, the MCA module is then used to change the feature subset into the triangle area map (TAM) matrix, and finally inputs the TAM matrix into the LSTM module for the training and testing purpose. To better demonstrate the performance of the proposed model, it is compared with those of convolutional neural networks, recurrent neural network, deep forest, support vector machine, and k-nearest neighbour methods proposed by the previous researchers. Experimental results show that the testing accuracy of the proposed model on 5-classification task using NSL-KDD dataset is up to 82.15%, and that on 10-classification task using UNSW-NB15 dataset is up to 77.74%. Moreover, compared with the traditional machine learning and existing deep learning models, the proposed model has shown to achieve better classification detection performance. |
资助项目 | National Natural Science Foundation of China[61862041][61363078] |
WOS研究方向 | Computer Science |
语种 | 英语 |
出版者 | INST ENGINEERING TECHNOLOGY-IET |
WOS记录号 | WOS:000515534600003 |
状态 | 已发表 |
内容类型 | 期刊论文 |
源URL | [http://ir.lut.edu.cn/handle/2XXMBERH/56777] |
专题 | 计算机与通信学院 |
通讯作者 | Zhang, Qiu-Yu |
作者单位 | Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China |
推荐引用方式 GB/T 7714 | Dong, Rui-Hong,Li, Xue-Yong,Zhang, Qiu-Yu,et al. Network intrusion detection model based on multivariate correlation analysis - long short-time memory network[J]. IET INFORMATION SECURITY,2020,14(2):166-174. |
APA | Dong, Rui-Hong,Li, Xue-Yong,Zhang, Qiu-Yu,&Yuan, Hui.(2020).Network intrusion detection model based on multivariate correlation analysis - long short-time memory network.IET INFORMATION SECURITY,14(2),166-174. |
MLA | Dong, Rui-Hong,et al."Network intrusion detection model based on multivariate correlation analysis - long short-time memory network".IET INFORMATION SECURITY 14.2(2020):166-174. |
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