Network Traffic Classification using Machine Learning for Software Defined Networks

The recent development in industry automation and connected devices made a huge demand for network resources. Traditional networks are becoming less effective to handle this large number of traffic generated by these technologies. At the same time, Software defined networking (SDN) introduced a programmable and scalable networking solution that enables Machine Learning (ML) applications to automate networks. Issues with traditional methods to classify network traffic and allocate resources can be solved by this SDN solution. Network data gathered by the SDN controller will allow data analytics methods to analyze and apply machine learning models to customize the network management. This paper has focused on analyzing network data and implement a network traffic classification solution using machine learning and integrate the model in software-defined networking platform

FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning

peer reviewedFrom a telecommunication standpoint, the surge in users and services challenges next-generation networks with escalating traffic demands and limited resources. Accurate traffic prediction can offer network operators valuable insights into network conditions and suggest optimal allocation policies. Recently, spatio-temporal forecasting, employing Graph Neural Networks (GNNs), has emerged as a promising method for cellular traffic prediction. However, existing studies, inspired by road traffic forecasting formulations, overlook the dynamic deployment and removal of base stations, requiring the GNN-based forecaster to handle an evolving graph. This work introduces a novel inductive learning scheme and a generalizable GNN-based forecasting model that can process diverse graphs of cellular traffic with one-time training. We also demonstrate that this model can be easily leveraged by transfer learning with minimal effort, making it applicable to different areas. Experimental results show up to 9.8% performance improvement compared to the state-of-the-art, especially in rare-data settings with training data reduced to below 20%

Optimising QoE for Scalable Video multicast over WLAN

International audienceQuality of Experience (QoE) is the key to success for multimedia applications and perceptual video quality is one of the important component of QoE. A recent video encoding scheme called Scalable Video Coding (SVC) provides the flexibility and the capability to adapt the video quality to varying network conditions and heterogeneous users. In this paper, we focus on SVC multicast over IEEE 802.11 networks. Traditionally, multicast uses the lowest modulation resulting in a video with only base quality even for users with good channel conditions. To optimize QoE, we propose to use multiple multicast sessions with different transmission rates for different SVC layers. The goal is to provide at least the multicast session with acceptable quality to users with bad channel conditions and to provide additional multicast sessions having SVC enhancement layers to users with better channel conditions. The selection of modulation rate for each SVC layer and for each multicast session is achieved with binary integer linear programming depending on network conditions with a goal to maximize global QoE. Results show that our algorithm maximizes global QoE by providing highest quality videos to users with good channel conditions and by guaranteeing at least acceptable QoE for all users

A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions

peer reviewedThe rapid advances in the Internet of Things (IoT) have promoted a revolution in communication technology and offered various customer services. Artificial intelligence (AI) techniques have been exploited to facilitate IoT operations and maximize their potential in modern application scenarios. In particular, the convergence of IoT and AI has led to a new networking paradigm called Intelligent IoT (IIoT), which has the potential to significantly transform businesses and industrial domains. This paper presents a comprehensive survey of IIoT by investigating its significant applications in mobile networks, as well as its associated security and privacy issues. Specifically, we explore and discuss the roles of IIoT in a wide range of key application domains, from smart healthcare and smart cities to smart transportation and smart industries. Through such extensive discussions, we investigate important security issues in IIoT networks, where network attacks, confidentiality, integrity, and intrusion are analyzed, along with a discussion of potential countermeasures. Privacy issues in IIoT networks were also surveyed and discussed, including data, location, and model privacy leakage. Finally, we outline several key challenges and highlight potential research directions in this important area

Gestion de ressources basée sur la qualité dans les réseaux sans-fil

Wireless multimedia networking is gaining tremendous success nowadays. Due to their characteristics (limited bandwidth, variable radio conditions, greater interference, etc.), the need of more efficient management has become crucial. Meanwhile, traditional ways of managing network, using information from monitoring technical parameters (loss, delays, jitter, etc.), fail to give accurate evaluations of user experience or Quality of Experience (QoE). In this thesis, new methods based on QoE indicator have been proposed to solve these problems. The propositions are admission control, rate adaptation, and packet scheduling regarding network operator as well as network selection regarding user side. The real-time measurement of QoE is accomplished with PSQA (Pseudo-Subjective Quality Assessment) tool. The simulations have been conducted using different wireless technologies both in homogeneous and heterogeneous environment. The obtained results encourage the use of QoE concept in further research, which could pave the road to a new paradigm of resource management.Les applications multimédias pour terminaux mobiles connaissent un succès grandissant. Cela oblige à développer de nouvelles méthodes plus efficaces de gestion des ressources des réseaux sans-fil du fait de leurs caractéristiques particulières : bande-passante limitée, état radio variable, interférences plus importantes, etc. Par ailleurs, les méthodes classiques de la gestion de ressources basées sur des paramètres techniques (perte/retard de paquets, gigue, etc.) ne parviennent pas à donner des évaluations précises de la qualité telle que perçue (encore appelée Qualité d'Expérience ou QdE) par l'utilisateur de ces applications. Cette thèse s'appuie sur une technique hybride nommée PSQA (Pseudo-Subjective Quality Assessment) d'évaluation pseudo-subjective en temps réel de la QdE pour proposer de nouvelles méthodes de gestion de ressources dans les réseaux multimédias sans-fil. Que ce soit du côté de l'opérateur réseau ou du côté de l'utilisateur, nous avons proposé des méthodes de contrôle d'accès et d'ordonnancement ainsi que des méthodes de sélection de réseaux d'accès dans le contexte des réseaux sans-fil hétérogènes utilisant différentes technologies (IEEE 802.11, UMTS, etc.). Les résultats obtenus encouragent l'utilisation du concept de QdE et ouvre la voie à un nouveau paradigme dans la gestion des ressources dans les réseaux multimédias sans-fil

Gestion de ressources basée sur la qualité dans les réseaux sans-fil

Wireless multimedia networking is gaining tremendous success nowadays. Due to their characteristics (limited bandwidth, variable radio conditions, greater interference, etc.), the need of more efficient management has become crucial. Meanwhile, traditional ways of managing network, using information from monitoring technical parameters (loss, delays, jitter, etc.), fail to give accurate evaluations of user experience or Quality of Experience (QoE). In this thesis, new methods based on QoE indicator have been proposed to solve these problems. The propositions are admission control, rate adaptation, and packet scheduling regarding network operator as well as network selection regarding user side. The real-time measurement of QoE is accomplished with PSQA (Pseudo-Subjective Quality Assessment) tool. The simulations have been conducted using different wireless technologies both in homogeneous and heterogeneous environment. The obtained results encourage the use of QoE concept in further research, which could pave the road to a new paradigm of resource management.Les applications multimédias pour terminaux mobiles connaissent un succès grandissant. Cela oblige à développer de nouvelles méthodes plus efficaces de gestion des ressources des réseaux sans-fil du fait de leurs caractéristiques particulières : bande-passante limitée, état radio variable, interférences plus importantes, etc. Par ailleurs, les méthodes classiques de la gestion de ressources basées sur des paramètres techniques (perte/retard de paquets, gigue, etc.) ne parviennent pas à donner des évaluations précises de la qualité telle que perçue (encore appelée Qualité d'Expérience ou QdE) par l'utilisateur de ces applications. Cette thèse s'appuie sur une technique hybride nommée PSQA (Pseudo-Subjective Quality Assessment) d'évaluation pseudo-subjective en temps réel de la QdE pour proposer de nouvelles méthodes de gestion de ressources dans les réseaux multimédias sans-fil. Que ce soit du côté de l'opérateur réseau ou du côté de l'utilisateur, nous avons proposé des méthodes de contrôle d'accès et d'ordonnancement ainsi que des méthodes de sélection de réseaux d'accès dans le contexte des réseaux sans-fil hétérogènes utilisant différentes technologies (IEEE 802.11, UMTS, etc.). Les résultats obtenus encouragent l'utilisation du concept de QdE et ouvre la voie à un nouveau paradigme dans la gestion des ressources dans les réseaux multimédias sans-fil.RENNES1-BU Sciences Philo (352382102) / SudocSudocFranceF

Gestion de ressources basée sur la qualité dans les réseaux sans-fil

Wireless multimedia networking is gaining tremendous success nowadays. Due to their characteristics (limited bandwidth, variable radio conditions, greater interference, etc.), the need of more efficient management has become crucial. Meanwhile, traditional ways of managing network, using information from monitoring technical parameters (loss, delays, jitter, etc.), fail to give accurate evaluations of user experience or Quality of Experience (QoE). In this thesis, new methods based on QoE indicator have been proposed to solve these problems. The propositions are admission control, rate adaptation, and packet scheduling regarding network operator as well as network selection regarding user side. The real-time measurement of QoE is accomplished with PSQA (Pseudo-Subjective Quality Assessment) tool. The simulations have been conducted using different wireless technologies both in homogeneous and heterogeneous environment. The obtained results encourage the use of QoE concept in further research, which could pave the road to a new paradigm of resource management.Les applications multimédias pour terminaux mobiles connaissent un succès grandissant. Cela oblige à développer de nouvelles méthodes plus efficaces de gestion des ressources des réseaux sans-fil du fait de leurs caractéristiques particulières : bande-passante limitée, état radio variable, interférences plus importantes, etc. Par ailleurs, les méthodes classiques de la gestion de ressources basées sur des paramètres techniques (perte/retard de paquets, gigue, etc.) ne parviennent pas à donner des évaluations précises de la qualité telle que perçue (encore appelée Qualité d'Expérience ou QdE) par l'utilisateur de ces applications. Cette thèse s'appuie sur une technique hybride nommée PSQA (Pseudo-Subjective Quality Assessment) d'évaluation pseudo-subjective en temps réel de la QdE pour proposer de nouvelles méthodes de gestion de ressources dans les réseaux multimédias sans-fil. Que ce soit du côté de l'opérateur réseau ou du côté de l'utilisateur, nous avons proposé des méthodes de contrôle d'accès et d'ordonnancement ainsi que des méthodes de sélection de réseaux d'accès dans le contexte des réseaux sans-fil hétérogènes utilisant différentes technologies (IEEE 802.11, UMTS, etc.). Les résultats obtenus encouragent l'utilisation du concept de QdE et ouvre la voie à un nouveau paradigme dans la gestion des ressources dans les réseaux multimédias sans-fil

SURFS: Sustainable intrUsion detection with hieraRchical Federated Spiking neural networks

peer reviewedThe rapid proliferation of Internet of Things (IoT) devices and the transition to distributed computing environments necessitate advanced intrusion detection systems (IDS) to safeguard the new paradigm known as Cloud-Edge-IoT (CEI) continuum. In this paper, we introduce a novel approach called SURFS, integrating Hierarchical Federated Learning (HFL) with Spiking Neural Networks (SNN) to propose a robust, sustainable, and energy-efficient IDS for this continuum. HFL, with its hierarchical learning strategy, keeps data where they are generated, thus preserving user privacy and reducing communication overhead through its combination of decentralized and centralized architecture. On the other hand, SNN, inspired by human neural mechanisms, offers significant computational efficiency. Our proposed IDS combines these strengths, facilitating localized and energy-efficient detection at the edge and IoT layers while enabling global model aggregation and updates at the cloud layer. Through extensive experiments using one of the most recent datasets (Edge-IIoTset), we demonstrate that our approach not only detects attacks with high accuracy but also substantially reduces energy consumption across the continuum. The SURFS model presents a slightly superior performance in classification accuracy, outstripping the FL+SNN and non-FL models by margins of 0.5% and 1.21%; however, with a much faster convergence time (3× and 17× respectively). In terms of sustainability, it achieves a remarkable reduction in communication overhead 99% lower than FL+SNN and 97% lower than non-FL. Additionally, it showcases significant improvements in computational cost, being 62% more efficient than FL+SNN and 94% more efficient than the non-FL model

F-BIDS: Federated-Blending based Intrusion Detection System

International audienceThe 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

Towards a Scalable and Energy-Efficient Framework for Industrial Cloud-Edge-IoT Continuum

peer reviewedThe Cloud-Edge-IoT (CEI) continuum integrates edge computing, cloud computing, and the Internet of Things (IoT), fostering rapid Industrial Internet of Things (IIoT) development. Despite its potential, it faces significant challenges, including robustness issues, communication-induced latency, and inconsistent model convergence due to system and data heterogeneity. Machine Learning (ML), a vital technology in this domain, further complicates privacy and overhead concerns. To mitigate these issues, Federated Learning (FL) appeared as a promising solution where the FL setting allows the devices to collaboratively train a model while keeping training data local. However, in practice, it suffers from several issues such as robustness (due to a single point of failure), latency (it still requires a significant amount of communication resources), and model convergence (due to the heterogeneity of system and statistics). To cope with these issues, we propose to integrate Hierarchical FL (HFL) and Spiking Neural Networks (SNN) into the framework for building a scalable and energy-efficient solution for the industrial CEI continuum. We present an in-depth overview, discussions on emerging applications, and a performance evaluation via a case study in IoT image classification. We also identify and explore open research topics crucial for the future realization of such a continuum