An Empirical Study on Network Anomaly Detection using Convolutional Neural Networks Conference Paper uri icon

abstract

  • 2018 IEEE. Deep learning has been widely applied to network anomaly detection to improve performance. In our past research, we empirically evaluated a set of deep learning models, including Fully Connected Network (FCN), Variational Auto Encoder (VAE), and Sequence to Sequence model with Long Short-Term Memory (Seq2Seq-LSTM), for network anomaly detection. Additionally, we evaluate Convolution Neural Networks (CNNs) for network anomaly detection in this paper. We set up three simple CNN models with different internal depths (shallow CNN, moderate CNN, and deep CNN) to see the impact of the depth to the performance. We evaluate the models using three different types of traffic datasets. Our experimental results show that deeper structures do not make any performance improvement. In addition, we observed that the evaluated CNN models occasionally outperform the VAE models, but do not work better than the other deep learning models based on FCN and Seq2Seq-LSTM.

name of conference

  • 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)

published proceedings

  • 2018 IEEE 38TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS)

author list (cited authors)

  • Kwon, D., Natarajan, K., Suh, S. C., Kim, H., & Kim, J.

citation count

  • 72

complete list of authors

  • Kwon, Donghwoon||Natarajan, Kathiravan||Suh, Sang C||Kim, Hyunjoo||Kim, Jinoh

publication date

  • July 2018