期刊
IET COMMUNICATIONS
卷 14, 期 5, 页码 888-895出版社
WILEY
DOI: 10.1049/iet-com.2019.0172
关键词
feature extraction; pattern classification; telecommunication security; security of data; learning (artificial intelligence); wireless sensor networks; data mining; decision trees; convolutional neural nets; fuzzy neural nets; fuzzy temporal decision tree classification; wireless sensor networks; intrusion detection system; intelligent decision tree classification algorithm; dynamic recursive feature selection algorithm; intelligent fuzzy temporal decision tree algorithm; convolution neural networks; network trace data; network performance; KDD cup data set; false positive rate; energy consumption; packet delivery ratio
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.
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