Authors:
Abderahmane Hamdouchi
and
Ali Idri
Affiliation:
Mohammed VI Polytechnic University, Benguerir, Morocco
Keyword(s):
Intrusion Detection System, Federated Learning, Deep Learning, Netflow, IoT, Cybersecurity.
Abstract:
The Internet of Things (IoT) involves billions of interconnected devices, making IoT networks vulnerable to cyber threats. To enhance security, deep learning (DL) techniques are increasingly used in intrusion detection systems (IDS). However, centralized DL-based IDSs raise privacy concerns, prompting interest in Federated Learning (FL). This research evaluates FL configurations using dense neural networks (DNN) and convolutional neural networks (CNN) with two optimizers, stochastic gradient descent (SGD) and Adam, across 20% and 60% feature thresholds. Two cost-sensitive learning techniques were applied: undersampling with binary cross-entropy and weighted classes using weighted binary cross-entropy. Using the NF-ToN-IoT-v2 dataset, 16 FL configurations were analyzed. Results indicate that SGD, combined with CNN and the Undersampling technique applied to the top 7 features, outperformed other configurations.