Classification of Malicious Network Dataset With Residual CNN

In this study, a model on network security is proposed and a method is suggested for data protection, integrity, and communication continuity. Network security is becoming more and more important every day as the digital world develops. It is aimed at classifying the data labeled as good and bad in the ready dataset. In the proposed model, first of all, all the information in the dataset is digitized. Then, it is normalized to the range of 0-1 and made ready as an input to the proposed architecture. It is aimed to classify the information in this two-class dataset with the proposed Residual CNN architecture. The accuracy rate obtained after the training and testing stages of the model is 94.9%. This accuracy rate shows that the proposed model successfully results in the detection of malicious packets in network attacks and can be used for network security.