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Y. Zhou et al.
References
1. Almseidin M, Alzubi M, Kovacs S et al (2017) Evaluation of machine learning
algorithms for intrusion detection system. In: 15th IEEE international symposium
on intelligent systems and informatics. IEEE, pp 277–282
2. Amudha P, Karthik S, Sivakumari S (2014) Classification techniques for intrusion
detection-an overview. Int J Comput Appl 76(16):33–40
3. Godin F, Degrave J, Dambre J, De Neve W (2018) Dual rectified linear units
(DReLUs): a replacement for Tanh activation functions in quasi-recurrent neural
networks. Pattern Recogn Lett 116:8–14
4. Samrin R, Vasumathi D (2018) Hybrid weighted K-means clustering and artificial
neural network for an anomaly-based network intrusion detection system. J Intell
Syst 27(2):135–147
5. Divyasree TH, Sherly KK (2018) A network intrusion detection system based on
ensemble CVM using efficient feature selection approach. Procedia Comput Sci
143:442–449
6. Hu J, Zhang J, Zhang C, Wang J (2016) A new deep neural network based on
a stack of single-hidden-layer feedforward neural networks with randomly fixed
hidden neurons. Neurocomputing 171:63–72
7. Mingyu Q, Ming L, Yanming F (2015) Research on network intrusion detection
using machines based on principal component support vector analysis. Netinfo
Secur 15(2):15–18
8. Ludwig SA (2019) Applying a neural network ensemble to intrusion detection. J
Artif Intell Soft Comput Res 9(3):177–188
9. Liang Y, Niu D, Hong WC (2019) Short term load forecasting based on feature
extraction and improved general regression neural network model. Energy 166:653–
663
10. Srivastava N, Hiton GE, Krizhevsky A et al (2014) Dropout: a simple way to
prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958