Network anomaly detection using channel boosted and residual learning based deep convolutional neural network期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

Network anomaly detection using channel boosted and residual learning based deep convolutional neural network

Affiliation:	1. School of Accounting, Chongqing University of Technology, Chongqing 400044, China;2. School of Computer Science, Chongqing University, Chongqing 400044, China;3. ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS), School of Mathematical Science, University of Adelaide, Adelaide, SA 5005, Australia;4. IIJ, Japan;5. Image Intelligence, Australia;1. School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China;2. School of Electronic and Information Engineering, Lanzhou Institute of Technology, Lanzhou, Gansu, China

Abstract:

Keywords:	Network anomaly detection Autoencoder Channel boosted CNN Residual learning Reconstructed feature space Deep Learning
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