Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks

Intrusion detection systems assume a noteworthy job in the provision of security in wireless Sensor networks. The existing intrusion detection systems focus only on the detection of the known types of attacks. However, it neglects to recognise the new types of attacks, which are introduced by malici...

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Veröffentlicht in:	IET communications Jg. 14; H. 5; S. 888 - 895
Hauptverfasser:	Nancy, Periasamy, Muthurajkumar, S, Ganapathy, S, Santhosh Kumar, S.V.N, Selvi, M, Arputharaj, Kannan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 17.03.2020
Schlagworte:	convolution neural networks convolutional neural nets data mining decision trees dynamic recursive feature selection algorithm energy consumption false positive rate feature extraction fuzzy neural nets fuzzy temporal decision tree classification intelligent decision tree classification algorithm intelligent fuzzy temporal decision tree algorithm intrusion detection system KDD cup data set learning (artificial intelligence) network performance network trace data packet delivery ratio pattern classification Research Article security of data telecommunication security wireless sensor networks telecommunication security intelligent decision tree classification algorithm pattern classification KDD cup data set false positive rate wireless sensor networks data mining fuzzy neural nets convolution neural networks dynamic recursive feature selection algorithm packet delivery ratio fuzzy temporal decision tree classification intrusion detection system security of data intelligent fuzzy temporal decision tree algorithm network performance feature extraction decision trees convolutional neural nets learning (artificial intelligence) network trace data energy consumption
ISSN:	1751-8628, 1751-8636
Online-Zugang:	Volltext
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