An Intrusion Detection Model based on a Convolutional Neural Network

Author:

Kim Jiyeon,Shin Yulim,Choi Eunjung

Funder

National Research Foundation of Korea

Seoul Women`s University

Publisher

Korea Multimedia Society - English Version Journal

Reference28 articles.

1. Jiyeon Kim, Yulim Ahn, and Eunjung Choi, “Network Intrusion Detection using Machine Learning Techniques”, in Proceeding of International Conference on Culture Technology 2019, August 2019.

2. Hasan, Md. Al & Nasser, Mohammed & Pal, Biprodip & Ahmad, Shamim, “Support Vector Machine and Random Forest Modeling for Intrusion Detection System (IDS),” Journal of Intelligent Learning Systems and Applications, vol. 06, pp. 45-52, 2014. 10.4236/jilsa.2014.61005

3. Mulay, Snehal & Devale, P.R. & Garje, Goraksh, “Intrusion Detection System Using Support Vector Machine and Decision Tree,” International Journal of Computer Applications vol. 3. 10.5120/758-993, 2010. 10.5120/758-993

4. Beghad, Rachid, “Training all the KDD data set to classif and detect attacks,” Neural Network World, vol. 17, pp. 81-91, 2017.

5. Jia, F. & Kong, L.-Z., “Intrusion Detection Algorithm Based on Convolutional Neural Network,” Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, vol. 37, pp. 1271-1275, 2017.

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