TL;DR
A lightweight federated learning-based IDS that combines Random Forest for feature selection and Temporal Convolutional Network (TCN) for deep learning classification is presented that achieves superior attack-detection performance.
Federated Learning is investigated as a decentralized approach to intrusion detection that enables local model training on IoT edge devices while transmitting only encrypted model updates to a central server, thereby preserving data privacy and reducing communication overhead.
The results show a success in implementing a real time, scalable, privacy-preserving, and adaptive IDS in large-scale IoT deployments through intelligent workload distribution between edge and cloud layers.
FedShield-IDS is proposed, a novel federated intrusion detection framework that integrates a hybrid one-dimensional Convolutional Neural Network with Long Short-Term Memory units to simultaneously capture spatial traffic fingerprints and long-range temporal attack dynamics across IoT edge devices.
The proposed accurate and interpretable framework shows strong potential as an edge-deployable security solution for safeguarding IoT devices and improving cyber resilience.
A hierarchical privacy protection and poisoning-robust defense framework for industrial federated learning is proposed and can effectively suppress global-model degradation under multiple poisoning attacks and achieves a favorable balance among privacy protection strength, robustness, and training efficiency.
An Internet of Things-based, privacy-preserving Federated Learning (FL) framework for predicting machine failures in Industry 5.0 is proposed, addressing the frequently neglected concerns of data privacy and decentralized operational settings.
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