Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network

With the popularity of wireless networks, wireless sensor networks (WSNs) have advanced rapidly, and their flexibility and ease of deployment have resulted in more security concerns, making it critical to research network intrusion prevention for WSNs. Denial of service (DoS) is a common network att...

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Veröffentlicht in:	IEEE access 2022, Vol.10, p.103136-103149
Hauptverfasser:	Yao, Chengpeng, Yang, Yu, Yin, Kun, Yang, Jinwei
Format:	Artikel
Sprache:	eng
Schlagworte:	Anomalies Anomaly detection Artificial neural networks Classification Convolutional neural networks deep convolution neural network denial of service Denial of service attacks Feature extraction Machine learning network attack Neural networks Principal component analysis Principal components analysis Representation learning Storage capacity Telecommunication traffic Traffic capacity Traffic models Wireless networks Wireless sensor networks
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