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    CLASSIFICATION OF MALICIOUS NETWORK DATASET WITH RESIDUAL CNN
    (2025) Karaduman, Mucahit; YALÇIN, Sercan; YILDIRIM, Muhammed
    This paper proposes a Residual Convolutional Neural Network (CNN) based model for malicious traffic detection. Network security is becoming increasingly important every day as the digital world develops. It aims to classify the data labeled as benign and malicious 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 Convolutional Neural Network (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.