DNNIDS: A Novel Network Intrusion Detection Based on Deep Neural Network
Chia-Fen Hsieh, Che-Min Su · 2021
With the rapid development of the network, network security is a relatively important issue. However, traditional intrusion detection systems based on feature selection and classification have some drawbacks, such as processing redundant information and increasing computational time. This paper proposes Intrusion Detection System based on Deep Neural Network (DNNIDS). Our method includes preprocessing stage, model establishment stage, and test stage. Deep Learning (DL) can automatically extract features. Compared with other methods, this method can improve the accuracy to detect attack types.