Cyber security attack recognition on cloud computing networks based on graph convolutional neural network and graphsage models
Fargana J. Abdullayeva, Suleyman Suleymanzade
Abstract
In this paper, the modeling of the network attacks of cloud computing through Graph Neural Networks is considered. Based on structural features and relationships between neighboring nodes and the edges of the cloud ecosystem a cyberattack detection method is proposed. A simulation dataset is created on the CSE-CIC-IDS2018 dataset to train and test the proposed graph neural network based models. In a comparative analysis of the suggested method with the existing one superior results are obtained from the model constructed on the GraphSAGE algorithm. Thus in the recognition of dataset samples, the model obtained a value of 0.97739 according to the accuracy metric. The values obtained by the algorithm on precision, recall, and F1-score metrics were also higher compared to the Graph Convolutional Neural Network model.