SVM Based Intrusion Detection Method with Nonlinear Scaling and Feature Selection-Reference-Cited by-同舟云学术

SVM Based Intrusion Detection Method with Nonlinear Scaling and Feature Selection

Published:2022-05-01 Issue:5 Volume:E105.D Page:1024-1038
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

ZHANG Fei¹,ZHEN Peining²,JING Dishan²,TANG Xiaotang²,CHEN Hai-Bao²,YAN Jie¹

Affiliation:

1. Institute of Aeronautics and Astronautics, Northwestern Polytechnic University

2. Department of Micro/Nano-electronics, Shanghai Jiao Tong University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E105.D/5/E105.D_2021EDP7184/_pdf

Reference51 articles.

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2. [2] P. Sangkatsanee, N. Wattanapongsakorn, and C. Charnsripinyo, “Practical real-time intrusion detection using machine learning approaches,” Computer Communications, vol.34, no.18, pp.2227-2235, 2011. 10.1016/j.comcom.2011.07.001

3. [3] M. Tavallaee, E. Bagheri, W. Lu, and A.A. Ghorbani, “A detailed analysis of the kdd cup 99 data set,” IEEE International Conference on Computational Intelligence for Security and Defense Applications, pp.53-58, 2009.

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