Using Long-Short-Term Memory Based Convolutional Neural Networks for Network Intrusion Detection-Reference-Cited by-同舟云学术

Using Long-Short-Term Memory Based Convolutional Neural Networks for Network Intrusion Detection

Published:2019 Issue: Volume: Page:86-94
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:
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Author:

Hsu Chia-Ming,Hsieh He-Yen,Prakosa Setya Widyawan,Azhari Muhammad Zulfan,Leu Jenq-Shiou

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-06158-6_9

Reference15 articles.

1. Revathi, S., Malathi, A.: A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. Int. J. Eng. Res. Technol. 2, 1848–1853 (2013)

2. Dhanabal, L., Shantharajah, S.P., Dr.: A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. In: International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, pp. 446–452 (2015)

3. Kuang, F., Xu, W., Zhang, S.: A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl. Soft Comput. 18, 178–184 (2014)

4. Hettich, S., Bay, S.D.: The UCI KDD Archive, University of California, Department of Information and Computer Science, Irvine, CA (1999). http://kdd.ics.uci.edu

5. Reddy, R.R., Ramadevi, Y., Sunitha, K.V.N.: Effective discriminant function for intrusion detection using SVM. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 11481153 (2016)

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