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| Title | A Hybrid Gan And Gcn Model For Intrusion Detection In Heterogenous Iot Networks |
|---|---|
| ID_Doc | 2169 |
| Authors | Alzahrani M.; Nanda P.; Mohanty M.; El Zegil F. |
| Year | 2025 |
| Published | 2025 17th International Conference on Computer and Automation Engineering, ICCAE 2025 |
| DOI | http://dx.doi.org/10.1109/ICCAE64891.2025.10980509 |
| Abstract | The Internet of Things (IoT) is an interconnection of millions of devices of various types, sensors and surveillance systems to collect and transmit the real-time. There has been a tremendous surge in the IoT with technological advancements in the last decade. The IoT has transformed and is now becoming a part of almost every sector including healthcare, manufacturing, smart cities, agriculture, home appliances and many more. The rapid growth in IoT can be attributed to several factors such as increased connectivity, advancement in technology, proliferation of IoT devices, etc. Although the rapid growth has resulted in several benefits, there has been an increase in the security vulnerabilities including botnet attacks, Denial of Service (DoS) attacks, data privacy concerns, IoT specific malware, etc. To ensure security and reduce the effect of risks associated with it, several solutions have been proposed such as security by design, IoT software regular update and patch management, advanced authentication and encryption mechanisms, network segmentation, Intrusion Detection Systems (IDS), etc. We propose an intrusion detection model based on Deep Learning (DL) strategies to detect the potential intrusions in the IoT network in real-time. In this study we implemented three DL based algorithms which include Generative Adversarial Networks (GAN), Graph Convolution Network (GCN) and a hybrid model implemented by integrating the GAN and GCN. Among the three implemented algorithms the proposed hybrid model achieved the highest performance with an accuracy of 97.5 % followed by GCN gaining an accuracy of 96.3 % and GAN with 89.6 % accuracy. The other performance metrices include precision, recall, f1-score, False Positive Rate (FPR) and False Negative Rate (FNR). © 2025 IEEE. |
| Author Keywords | deep learning; generative adversarial networks; graph convolution networks; graph neural networks; machine learning |
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