Survey on applications of graph neural networks in cyber security

Abstract

The recent success of neural networks has boosted research on pattern recognition and data mining. Machine learning tasks like object detection, machine translation, and speech recognition, have been given new life with end-to-end deep learning paradigms like CNN, RNN etc. Deep Learning is good at capturing hidden patterns of Euclidean data (images, text, videos). But what about applications where data is generated from non-Euclidean domains, represented as graphs with complex relationships and inter-dependencies between objects? That’s where Graph Neural Networks (GNN) come in. Graph Neural Networks (GNNs) are a class of deep learning methods designed to perform inference on data described by graphs. GNNs are neural networks that can be directly applied to graphs, and provide an easy way to do node-level, edge-level, and graph-level prediction tasks. In this thesis we tend to observe various types of GNNs and the use of it.Then we tend to survey the use of GNNs on cybersecuirty aspects which means how well we can use GNNs in the field of cybersecurity effectively. We tend to use GNN and it’s use through some experiments which we have shown at the later part of this thesis