Robust Attacks Detection Model for Internet of Flying Things Based on Generative Adversarial Network (GAN) and Adversarial Training

Gaber, Tarek ORCID: https://orcid.org/0000-0003-4065-4191, Ali, Tarek ORCID: https://orcid.org/0000-0002-8380-1625, Nicho, Mathew ORCID: https://orcid.org/0000-0001-7129-3988 and Torky, Mohamed ORCID: https://orcid.org/0000-0002-3229-9794 (2025) Robust Attacks Detection Model for Internet of Flying Things Based on Generative Adversarial Network (GAN) and Adversarial Training. IEEE Internet of Things Journal, 12 (13). pp. 23961-23974. ISSN 2372-2541

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Abstract

The Internet of Flying Things (IoFT) holds significant promise in fields like disaster management and surveillance. However, it is increasingly vulnerable to cyberattacks that can compromise the confidentiality, integrity, and availability (CIA) of sensitive data. Despite the growing interest in proposing intrusion detection systems (IDSs) for IoFT networks, current literature faces key limitations, particularly the shortage of publicly available IoFT datasets with diverse attacks, and the fact that existing IDSs lack robustness against sophisticated adversarial machine learning (ML) attacks. This article is the first study to address these limitations by proposing a more resilient and accurate IDS tailored for IoFT networks (RIDSIoFT). We introduce a novel IDS that leverages generative adversarial networks (GANs) to generate a hybrid dataset that combines real IoFT traffic data with GAN-generated adversarial attacks, addressing the dataset diversity issue. Additionally, we introduce an innovative adversarial training method to enhance the system’s defense against evolving threats, such as fast gradient sign method (FGSM), basic iterative method (BIM), and Carlini & Wagner (C&W) attacks. The proposed RIDS-IoFT was evaluated using four ML models, random forest (RF), decision tree (DT), support vector machine (SVM), and logistic regression (LR), on two datasets: 1) ECU-IoFT and 2) CICIDS2018. The IDS’s performance was assessed based on its ability to detect both traditional and adversarial attacks. The results show that the RF model achieved the highest detection accuracy, up to 96.5%, demonstrating superior performance across both real and hybrid datasets. The proposed RIDS-IoFT not only enhances detection accuracy but also strengthens resilience against adversarial threats, making it suitable for resource-constrained IoFT environments. In conclusion, this study presents a comprehensive approach to securing IoFT networks by combining real and synthetic data, improving IDS robustness and accuracy against both traditional and adversarial attacks.