This research shows that 6G networks may benefit from IoT systems that are safe, scalable, and privacy‐preserving when FL and GNNs operate together.
Abstract
Despite 6G networks quickly maturing, the Internet of Things (IoT) will be better connected than ever before with increased capacity for big devices, and the capability to run intelligent applications in real time. Cyber incursions, data breaches, and antagonistic interferences are all made possible by the significant security holes left by these technical advancements. Because these systems face scalability limitations, privacy concerns, and dynamic IoT environmental obstacles, centralized anomaly detection technologies do not solve security risks against IoT networks. This study presents a system for real‐time threat detection using Federated Learning (FL) that safeguards data privacy and tackles these current issues. Each client separately learns data patterns in the local model training layer using a hybrid architecture that combines adversarial training to increase robustness with Graph Neural Networks (GNNs) for spatial relationships and attention‐enhanced Long Short‐Term Memory (LSTM) for temporal dependencies. After that, the locally trained models are sent to the secure federated learning layer (without sharing raw data), where a central aggregator implements security measures like consistency screening and attack mitigation in addition to performing adaptive weighted aggregation. To protect the system from evasion attempts, the defense mechanism employs an adversarial defense protocol. The proposed framework anomaly detection accuracy, F1‐score, area under the curve (AUC), and detection rate are all 97.6%. 97.2%, 98.5%, and 14.1 ms, respectively. This research shows that 6G networks may benefit from IoT systems that are safe, scalable, and privacy‐preserving when FL and GNNs operate together.
Wireless Sensor Networks (WSNs) play a crucial role in various applications, but their vulnerability to malicious nodes and data breaches hinders their full potential. Traditional security methods often struggle to keep pace with evolving attack patterns and can introduce privacy concerns. This research proposes a novel framework for anomaly detection in WSNs that leverages federated deep learning and prioritizes real-time adaptation and data privacy. Sensor nodes collaboratively train adaptive deep learning models to identify anomalies in real-time, enabling continuous learning and response to evolving threats. Partial Homomorphic Encryption (PHE) safeguards sensitive data throughout the network, ensuring data confidentiality. The trade-off between security and computational cost associated with PHE is acknowledged. The effectiveness of the proposed system FedShield-PHE will be evaluated through simulations, comparing its performance to existing methods across various metrics including detection accuracy, network overhead, and energy consumption. This research offers a promising path forward for securing WSNs by enabling distributed, privacy-preserving anomaly detection with real-time adaptation capabilities.
N. Karthick, K. Ranjith Singh· International journal of com...· 0 citations
The Internet of Things (IoT) plays a vital role in the digital age by interconnecting numerous heterogeneous devices. This complexity and ubiquity expose IoT networks to diverse and sophisticated cyber threats. IoT environments require rigorous collaborative intrusion detection system (IDS) that operates in distributed environments and heterogeneous data traffic. While federated learning offers a promising, privacy-conscious training model, most traditional approaches to IDS fail to detect the structural relationships between connected entities. On the other hand, Graph Neural Networks (GNNs) have attracted significant attention in Network Intrusion Detection Systems (NIDS) for their effectiveness in modeling complex network traffic flows in real-world environments. However, existing GAD methods are generally designed for centralized training, thereby posing privacy leakage risks. Despite progress, current mainstream Federated Graph anomaly detection (FGAD) methods still face challenges. A key limitation is that most existing approaches focus on node-level analysis while disregarding inter-node relationships making them ineffective against sophisticated attacks. To tackle this problem, this paper presents an Edge Federated Graph Anomaly Detection (E-FGAD) framework for IoT environments that combines centralized self-supervised pre-training with distributed supervised learning over edge embeddings. During the supervised phase, parameters are optimized in a federated manner using FedAvg with FedProx and server momentum. Our framework operates on graphs, where nodes represent IP endpoints and edges represent NetFlow records with traffic statistics as features, aiming to capture the flow of interactions between entities. We evaluate experiments on two real-world datasets, NF-BoT-IoT-v3 and NF-ToN-IoT-v3, in both binary and multi-class settings. E-FGAD achieves a maximum detection accuracy of 99.32%, a Macro-F1 of 89.73%, and a Weighted-F1 of 99.2%. Our framework outperforms centralized and federated baselines, demonstrating its effectiveness in detecting attacks while preserving privacy.
Nuha A. Hamad, Khairul Azmi Abu Bakar, Faizan Qamar et al.· IEEE Access· 0 citations
Simulation of a Federated Learning framework for privacy-preserving anomaly detection tailored to heterogeneous IoT networks characterised by non-independent and identically distributed data, variable computational capacities, and intermittent connectivity indicates that the proposed method offers a practical, scalable, and regulation-compliant pathway toward trustworthy intrusion and anomaly detection in large-scale, heterogeneous IoT deployments.
Raushan Raj, B. L. Pal, Saurab Singh· International journal of com...· 0 citations
The rapid integration of Internet of Things (IoT) in the healthcare domain has led to the emergence of the Internet of Medical Things (IoMT), which introduces significant benefits in patient monitoring and real‐time medical services. However, IoMT networks are inherently vulnerable due to resource constraints, heterogeneous devices, and sensitivity of medical data. In this paper, we propose a novel federated learning‐based anomaly detection system (Fed‐ADS) designed specifically for IoMT networks. Our system leverages local training of lightweight ML models on resource‐constrained IoMT devices and employs secure model aggregation at the gateway to preserve privacy and avoid centralized data collection. To address real‐world challenges, we implement and evaluate our system on a real IoMT testbed using Raspberry Pi devices under various attack scenarios. Furthermore, we examine the impact of privacy‐preserving techniques such as differential privacy on detection accuracy and system overhead. The runtime evaluation shows that our approach achieves high detection accuracy (over 94%) with minimal CPU and memory usage (under 3%), making it suitable for practical deployment in medical environments.
Mahdi Ajdani, Maziar Asmani, Asif Ali Laghari· International Journal of Com...· 0 citations
With the rapid development of Industrial Internet of Things (IIoT), large amounts of sensor data generated by industrial devices and edge nodes have become the basis of intelligent manufacturing applications. Collaborative modeling on these distributed data is important for tasks such as anomaly detection, equipment monitoring, and predictive maintenance. Federated learning offers a practical way to train models without exposing raw sensor data, but it still faces privacy leakage and malicious poisoning attacks. To address these issues, this paper proposes a hierarchical privacy protection and poisoning-robust defense framework for industrial federated learning. Starting from the sensitivity differences among parameters at different model layers, the proposed method designs a hierarchical privacy-budget allocation strategy that enhances protection for sensitive information while minimizing the performance impact of perturbation. Meanwhile, a multi-layer, multi-feature anomaly-detection mechanism is adopted to identify malicious updates by jointly exploiting directional consistency, scale stability, and inter-layer similarity, and majority voting together with update clipping is used to further improve system robustness. Experiments on Fashion-MNIST, MVTec AD, and C-MAPSS demonstrate that the proposed method can effectively suppress global-model degradation under multiple poisoning attacks and achieves a favorable balance among privacy protection strength, robustness, and training efficiency.
Huan Yin, Cong Chen, Jing-Yi Zhang et al.· Italian National Conference...· 0 citations
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.
M. H. Alanazi, A. Mir, Asma A. Alhashmi et al.· Engineering Reports· 0 citations