1D-CNN based Model for Classification and Analysis of Network Attacks
Kuljeet Singh, Amit Mahajan, Vibhakar Mansotra
Abstract
With the advancement in technology and upsurge in network devices, more and more devices are getting connected to the network leading to more data and information on the network which emphasizes the security of the network to be of paramount importance. Malicious traffic must be detected in networks and machine learning or more precisely deep learning (DL), which is an upcoming approach, should be used for better detection. In this paper, Detection of attacks through a classification of traffic into normal and attack data is done using 1D-CNN, a special variant of convolutional neural network (CNN). For this, the CICIDS2017 dataset consisting of 14 attack types spread across 8 different files, is considered for evaluating model performance and various indicators like recall, precision, F1-score have been utilized. Separate 1D-CNN based DL models were built on individual sub-datasets as well as on combined datasets. Also, an evaluation of the model is done by comparing it with an artificial neural network (ANN) model. Experimental results have demonstrated that the proposed model has performed better and shown great capability in detecting network attacks as the majority of the class labels had achieved excellent scores in each of the evaluation indicators used.