A method of intrusion detection based on Attention-LSTM neural network

Author:

Yang Shuaichuang1,Tan Minsheng1,Xia Shiying1,Liu Fangju1

Affiliation:

1. School of Computer Science, University of South China, Hengyang, China

Publisher

ACM

Reference18 articles.

1. Feng H Ling M and Hou X et al. 2017. Study of network intrusion detection method based on SMOTE and GBDT. Application Research of Computers 34(12):3745. DOI=10.3969/j.issn.1001-3695.2017.12.051. Feng H Ling M and Hou X et al. 2017. Study of network intrusion detection method based on SMOTE and GBDT. Application Research of Computers 34(12):3745. DOI=10.3969/j.issn.1001-3695.2017.12.051.

2. Zhang S Xie X and Xu Y. 2019. Intrusion detection method based on a deep convolutional neural network. Journal of Tsinghua University(Science and Technology) 59(1): 44--52. DOI=10.16511/j.cnki.qhdxxb.2019.22.004. Zhang S Xie X and Xu Y. 2019. Intrusion detection method based on a deep convolutional neural network. Journal of Tsinghua University(Science and Technology) 59(1): 44--52. DOI=10.16511/j.cnki.qhdxxb.2019.22.004.

3. Raju E Sravanthi K. 2013. Network intrusion detection using Support Vector Machines. International Journal of Computer Science And Management Research 2(1):131 3--1319. Raju E Sravanthi K. 2013. Network intrusion detection using Support Vector Machines. International Journal of Computer Science And Management Research 2(1):131 3--1319.

4. Training a neural-network based intrusion detector to recognize novel attacks

5. 2016. High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning;Erfani S. M.;Pattern Recognition, 58(C), 121--134. DOI= https://doi.org/10.1016/j.patcog.,2016

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