Graph‐Based Generative Adversarial Network for Adaptive IoT Intrusion Detection

Abstract

The rapid expansion of the internet of things (IoT) has enabled large‐scale connectivity across healthcare, smart homes, industrial automation, and intelligent infrastructure. However, this growth has also increased the exposure of IoT environments to complex and evolving cyber threats. Traditional intrusion detection systems, particularly signature‐based approaches, are often ineffective against previously unseen attacks and struggle to adapt to the heterogeneous and dynamic nature of IoT traffic. To address these challenges, this study proposes a hybrid intrusion detection framework that combines generative adversarial learning with graph attention‐based modeling. The proposed model leverages adversarial data generation to improve the representation of minority attack classes and employs graph attention mechanisms to capture structural dependencies among communicating entities. The framework was evaluated using the UNSW‐NB15 dataset and compared with baseline deep learning models, including generative adversarial networks, graph convolutional networks, and graph attention networks. The proposed method achieved an accuracy of 81.23%, precision of 83.89%, recall of 78.01%, and F1‐score of 80.84% on the held‐out test set, while also reducing false‐positive and false‐negative rates relative to the comparison models. The results demonstrate the effectiveness of combining adversarial data augmentation with graph attention‐based representation learning under the controlled, offline evaluation conditions used in this study. Although the framework may be relevant to IoT and industrial cybersecurity applications, its scalability, real‐time performance, edge‐device feasibility, and effectiveness in operational environments require further experimental validation.