Design and Analysis of Intrusion Detection System via Neural Network, SVM, and Neuro-Fuzzy

Author:

Tiwari Abhishek,Ojha Sanjeev Kumar

Publisher

Springer Singapore

Reference18 articles.

1. Kumar, I., Virmani, J., Bhadauria, H.S.: A review of breast density classification methods. In: Proceeding of 2nd International Conference on Computing for Sustainable Global Development INDIACom, pp. 1960–1967 (2015)

2. Kumar, I., Virmani, J., Bhadauria, H.S.: Wavelet packet texture descriptors based four-class BIRADS breast tissue density classification. Procedia Comput. Sci. 70, 76–84 (2015)

3. Kumar, I., Bhadauria, H.S., Virmani, J., Thakur, S.: A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybern. Biomed. Eng. 37, 217–228 (2017)

4. Sabahi, F.: Intrusion detection: a survey. In: The Third International Conference on Systems and Networks Communications. IEEE Computer Society (2008)

5. Chandrasekhar, A.M. et al.: Intrusion detection technique by using k-means, fuzzy neural network and SVM classifiers. In: IEEE Xplore (2013). https://doi.org/10.1109/iccci201.6466310

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