Empirical study on multiclass classification‐based network intrusion detection
Author:
Affiliation:
1. Department of Computer EngineeringIstanbul Commerce University Istanbul Turkey
2. Department of Computer EngineeringIstanbul Kültür University Istanbul Turkey
Publisher
Wiley
Subject
Artificial Intelligence,Computational Mathematics
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1111/coin.12220
Reference102 articles.
1. MahmoodT AfzalU.Security analytics: big data analytics for cybersecurity: a review of trends techniques and tools. In: Proceedings of the 2013 2nd National Conference on Information Assurance (NCIA);2013;Rawalpindi Pakistan.
2. TangTA MhamdiL McLernonD ZaidiSAR GhoghoM.Deep learning approach for network intrusion detection in software defined networking. In: Proceedings of the 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM);2016;Fez Morocco.
3. DongB WangX.Comparison deep learning method to traditional methods using for network intrusion detection. In: Proceedings of the 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN);2016;Beijing China.
4. A tutorial survey of architectures, algorithms, and applications for deep learning;Deng L;APSIPA Trans Signal Inf Process,2014
5. DuX CaiY WangS ZhangL.Overview of deep learning. In: Proceedings of the 31st Youth Academic Annual Conference of Chinese Association of Automation;2016;Wuhan China.
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