Browsing by Author "FERTAS Mouad"

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A Federated Learning Framework for Collaborative and Privacy-Preserving IoT Security Attack Prediction
(university of ghardaia, 2026-06-22) ADDOUN Mohammed; FERTAS Mouad
The rapid expansion of the Internet of Things (IoT) has multiplied the attack surface exposed to cyber threats. Traditional intrusion detection systems rely on collecting network data at a central location, which raises serious privacy, bandwidth, and regulatory concerns in IoT environments where devices are heterogeneous, resource-constrained, and geographically distributed. Federated Learning (FL) offers a promising alternative by enabling devices to collaboratively train a shared model without sharing their raw data. This thesis investigates how a federated learning-based framework can be designed to enable distributed IoT devices to collaboratively predict security attacks, while preserving data privacy and handling device heterogeneity. We propose FedShield, a federated learning framework for multi-class IoT attack prediction, built around a lightweight deep learning classifier, Federated Averaging, and a mask-based secure aggregation mechanism. The framework is implemented in Python using TensorFlow and the Flower library, and evaluated on the TON_IoT dataset partitioned across non-IID clients. The experimental results show that the framework reaches strong detection performance on a non-IID partition of the TON_IoT dataset, and that mask-based secure aggregation can be added without measurable utility cost. The work confirms that federated learning is a credible building block for privacy-preserving IoT intrusion detection.