Deep Learning for IoT Security: Leveraging GNNs and Attention Networks

This study introduces an innovative methodology that combine between two powerful architectures, a graph base on attention network (GAT) with another graph based on àdeep learning algorithm called E-Graphsage, the vector result was given to a model based on a multilayer perceptron (MLP) to enhance the interpretation process. Graph Fusion is a smart implementation for a unique neural network technology for network intrusion detection systems (NIDS). Deep learning has many diversified and complex subfields; in fact, the graph neural networks (GNNs) stand out as one of the latest advancements in DL. graph-based data have proven a good potential to be particularly effective in leveraging the inherent structure difference. Within the scope of network intrusion detection systems (NIDS), flow records represent the information collected from networks that can be conveniently transformed into a graph format for training and evaluation purposes. Furthermore, the main idea of this study is to work with two fields of graphs that combines the GATSAGE and EGraphsage techniques and a multilayer perceptron (MLP) at the last to combine and classify Graphs. We leveraged graph neural networks to address the issue of intrusion detection in IoT networks, grounded on four distinctive datasets—two as Bot and two from Ton. This enabled us to inspect both edge-level interactions in the graph and the sophisticated topological structures characteristic of the network. We see that this approach is a notable enhancement in IoT network security through the use of flow-based data as an intrinsic aspect of analysis. By combining graph analysis and machine learning with an MLP architecture, we achieved superior performance on principal classification tests. Our method was extensively tested on four new NIDS datasets, affirming its effectiveness. We hope our results will encourage further work in this emerging area.