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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Vydáno v:	IET communications Ročník 14; číslo 5; s. 888 - 895
Hlavní autoři:	Nancy, Periasamy, Muthurajkumar, S, Ganapathy, S, Santhosh Kumar, S.V.N, Selvi, M, Arputharaj, Kannan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 17.03.2020
Témata:	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
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Popis
Shrnutí:	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 malicious users leading to vulnerability and information loss in the network. In order to address this challenge, a new intrusion detection system, which detects the known and unknown types of attacks using an intelligent decision tree classification algorithm, has been proposed. For this purpose, a novel feature selection algorithm named dynamic recursive feature selection algorithm, which selects an optimal number of features from the data set is proposed. In addition, an intelligent fuzzy temporal decision tree algorithm is also proposed by extending the decision tree algorithm and integrated with convolution neural networks to detect the intruders effectively. The experimental analysis carried out using KDD cup data set and network trace data set demonstrates the effectiveness of this proposed approach. It proved that the false positive rate, energy consumption, and delay are reduced in the proposed work. In addition, the proposed system increases the network performance through increased packet delivery ratio.
ISSN:	1751-8628 1751-8636
DOI:	10.1049/iet-com.2019.0172