Using convolutional neural networks to network intrusion detection for cyber threats
Wen‐Hui Lin, Hsiao-Chung Lin, Ping Wang, Baohua Wu, Jeng-Ying Tsai · 2018 IEEE International Conference on Applied System Invention (ICASI) · 2018
In practice, Defenders need a more efficient network detection approach which has the advantages of quick-responding learning capability of new network behavioural features for network intrusion detection purpose. In many applications the capability of Deep Learning techniques has been confirmed to outperform classic approaches. Accordingly, this study focused on network intrusion detection using convolutional neural networks (CNNs) based on LeNet-5 to classify the network threats. The experiment results show that the prediction accuracy of intrusion detection goes up to 99.65% with samples more than 10,000. The overall accuracy rate is 97.53%.