Traffic Classification (TC) is pivotal for network management, cybersecurity, and Quality of Experience (QoE) monitoring. However, while Deep Learning (DL) has significantly advanced TC, most existing works assume static, idealized conditions, overlooking key challenges of real-world deployments—such as traffic variability, routing asymmetries, out-of-order packet arrivals, and partial visibility at the Vantage Points (VPs). This motivates the need for robustness evaluations under such scenarios. In this work, we investigate the robustness of state-of-the-art (SOTA) TC models under realistic, yet controlled, perturbation scenarios. Specifically, we introduce novel, model-agnostic traffic perturbations—simulating time jitter, retransmissions, and partial visibility—to reflect conditions commonly encountered in live network traffic. We evaluate our approach on three public datasets—i.e., VPN-16 , MIRAGE-19 , and MIRAGE-24 —and show how Mimetic-Enhanced , a multimodal model, tends to outperform two representative single-modal counterparts both in terms of TC effectiveness on clean traffic and robustness under perturbations. Nonetheless, our analysis also reveals that multimodal models remain vulnerable under specific perturbation settings. To address this limitation, we propose a model-agnostic perturbation-aware training framework based on Supervised Data Augmentation ( Aug ) and Contrastive Learning ( CL )—considering both self-supervised and supervised variants. Unlike architecture-specific solutions, our approach operates at the learning strategy level , allowing it to be seamlessly applied to diverse classifiers without requiring structural modifications. Adopting Mimetic-Enhanced as a primary multimodal case study, we integrate the proposed strategies into its two-stage training pipeline. Experimental results demonstrate that perturbation-aware training not only improves TC effectiveness on clean (i.e., unperturbed) traffic—particularly when applied across both training stages—but also significantly strengthens the model’s robustness under diverse and realistic perturbation scenarios. Furthermore, we investigate Out-of-Distribution (OOD) detection, model calibration, and TC effectiveness in low-data regimes. Finally, we explicitly demonstrate the framework’s generalizability by validating it on other SOTA architectures, spanning both single- and multi-modal approaches.

Wi-Fi sensing based on Channel State Information (CSI) has witnessed considerable research activity in recent years. However, a critical literature analysis reveals that only a limited amount of proposals are potentially reproducible, with many works lacking essential experimental details, publicly available datasets, or accessible analysis code. This may impede the research progress and the subsequent transition of promising findings into practical applications. The objective of this work is to identify CSI-based sensing proposals that are potentially reproducible based on the published information. Our goal is to provide a focused review of resources that can serve as a concrete starting point for researchers and practitioners seeking to experiment with and advance the field of Wi-Fi sensing. We perform a comprehensive analysis of publicly available datasets (encompassing both the collection methodologies and the environmental characteristics) and existing sensing models, accompanied by their code, pre-processing steps, and evaluation procedures. Finally, we discuss what are the minimum requirements for truly verifiable contributions in this field, and outline the best practices for creating and sharing reproducible CSI-based sensing datasets and models.

Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for large-scale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management.

Network traffic analysis is essential for modern communication systems, focusing on tasks like traffic classification, prediction, and anomaly detection. While classical Machine Learning (ML) and Deep Learning (DL) methods have proven effective, their scalability and real-time performance can be limited by evolving traffic patterns and computational demands. Quantum Machine-Learning (QML) offers a promising alternative by utilizing quantum computing's parallelism. This paper examines QML's application in mobile traffic classification, comparing classical methods such as Multi-layer Perceptron (MLP) and Convolutional Neural Networks (CNNs) with Quantum Neural Networks (QNNs) using different embedding types. Our experiments, conducted on the MIRAGE-COVID-CCMA-2022 dataset, show that QNNs achieve competitive performance, indicating QML's potential for efficient large-scale traffic classification in future networks.

The lifestyle change originated from the COVID-19 pandemic has caused a measurable impact on Internet traffic in terms of volume and application mix, with a sudden increase in usage of communication-and-collaboration apps. In this work, we focus on four of these apps (Skype, Teams, Webex, and Zoom), whose traffic we collect, reliably label at fine (i.e. per-activity) granularity, and analyze from the viewpoint of traffic prediction. The outcome of this analysis is informative for a number of network management tasks, including monitoring, planning, resource provisioning, and (security) policy enforcement. To this aim, we employ state-of-the-art multitask deep learning approaches to assess to which degree the traffic generated by these apps and their different use cases (i.e. activities: audio-call, video-call, and chat) can be forecast at packet level. The experimental analysis investigates the performance of the considered deep learning architectures, in terms of both traffic-prediction accuracy and complexity, and the related trade-off. Equally important, our work is a first attempt at interpreting the results obtained by these predictors via eXplainable Artificial Intelligence (XAI).