Building Auto-Encoder Intrusion Detection System based on random forest feature selection

Machine learning techniques have been widely used in intrusion detection for many years. However, these techniques are still suffer from lack of labeled dataset, heavy overhead and low accuracy. To improve classification accuracy and reduce training time, this paper proposes an effective deep learni...

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Veröffentlicht in:	Computers & security Jg. 95; S. 101851 - 15
Hauptverfasser:	Li, XuKui, Chen, Wei, Zhang, Qianru, Wu, Lifa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier Ltd 01.08.2020 Elsevier Sequoia S.A
Schlagworte:	Accuracy Algorithms Auto-Encoder Classification Coders Deep learning Feature selection Intrusion Intrusion detection systems Machine learning Network Intrusion Detection System Network security Training Unsupervised clustering Deep learning Network security Network Intrusion Detection System Auto-Encoder Unsupervised clustering
ISSN:	0167-4048, 1872-6208
Online-Zugang:	Volltext
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