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Abstract We study the routing problem of scalable video coding video streaming over wireless mesh networks. In contrast to most of the conventional routing algorithms, our proposal focuses on optimizing users' satisfaction. The mean opinion score –an indicator of quality of experience (QoE) in video streaming– is utilized to assess the quality of routes in wireless mesh networks. The objective is to optimize the overall user experience in the network. Conventional routing approaches do not consider QoE and are not optimal with respect to user experience. Moreover, some centralized approaches are not scalable and require significant computational resources. The latter disadvantage can be overcome using distributed approaches. This paper presents a QoE-based cooperative distributed routing approach. Among distributed cooperative optimization schemes, AD3 is highlighted as one of the most efficient because of its fast convergence. The contributions of this paper are as follows: we encode the original problem into a factor graph and optimize the number of exchanged messages; we propose a partially distributed routing scheme based on OLSR and AD3; and we propose a distributed decoding algorithm in order to find a feasible solution. Our thorough simulation results confirm the advantages of the proposed scheme. [ABSTRACT FROM AUTHOR]

The rapid development of network communication along with the drastic increase in the number of smart devices has triggered a surge in network traffic, which can contain private data and in turn affect user privacy. Recently, Federated Learning (FL) has been proposed in Intrusion Detection Systems (IDS) to ensure attack detection, privacy preservation, and cost reduction, which are crucial issues in traditional centralized machine-learning-based IDS. However, FL-based approaches still exhibit vulnerabilities that can be exploited by adversaries to compromise user data. At the same time, meta-models (including the blending models) have been recognized as one of the solutions to improve generalization for attack detection and classification since they enhance generalization and predictive performances by combining multiple base models. Therefore, in this paper, we propose a Federated Blending model-driven IDS framework for the Internet of Things (IoT) and Industrial IoT (IIoT), called F-BIDS, in order to further protect the privacy of existing ML-based IDS. The proposition consists of a Decision Tree (DT) and Random Forest (RF) as base classifiers to first produce the meta-data. Then, the meta-classifier, which is a Neural Networks (NN) model, uses the meta-data during the federated training step, and finally, it makes the final classification on the test set. Specifically, in contrast to the classical FL approaches, the federated meta-classifier is trained on the meta-data (composite data) instead of user-sensitive data to further enhance privacy. To evaluate the performance of F-BIDS, we used the most recent and open cyber-security datasets, called Edge-IIoTset (published in 2022) and InSDN (in 2020). We chose these datasets because they are recent datasets and contain a large amount of network traffic including both malicious and benign traffic. [ABSTRACT FROM AUTHOR]