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
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Beschreibung
Zusammenfassung: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