Lists
Lists
Hamouda, Djallel, Ferrag, Mohamed Amine, Nadjette, Benhamida, Hamid, Seridi and Ghanem, Mohamed Chahine (2024) Revolutionizing intrusion detection in industrial IoT with distributed learning and deep generative techniques. Internet of Things, 26 ((2024)). p. 101149. ISSN 2542-6605
In response to escalating cyber threats and privacy issues within the Industrial Internet of Things (IIoT), this research presents FedGenID, an advanced Federated Generative Intrusion Detection System, to safeguard IIoT networks. Our approach introduces a three-model framework:
1) a federated generative model, incorporating a Conditional Generative Adversarial Network (cGANs) for data augmentation, emphasizing only generator model updates to be shared among clients. This model uses a Wasserstein loss function with Gradient Penalty to amplify sample diversity, indicative of varying cyber threats. Concurrently, we address the issues of imbalanced and distributed data and deploy a data curation technique to align generated data within specific constraints.
2) A secondary model fine-tunes local Critics for enhanced resilience and detection of various adversarial attacks.
3) The third model focuses on precise cyber threat identification, leveraging augmented data for improved training under a synthetic federated learning schema, bolstering detection capability, especially against zero-day threats. Our evaluation of FedGenID, utilizing a novel industrial cybersecurity dataset, highlights its efficacy in non-IID, multi-class cyber threat detection and its resilience to adversarial attacks. Furthermore, we demonstrate how FedGenID can mitigate the negative impact of differential privacy-enhanced FL on model performance.
The findings underscore FedGenID’s proficiency in detection accuracy, surpassing traditional FedID by 10% in the presence of zero-day attacks and high privacy regimes.
![[img]](/style/images/fileicons/text.png)
Restricted to Repository staff only until 6 March 2025.
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.
Download (1MB)
 |
View Item |