TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Jing Zhang, Chao Luo, Rui Shao	MTG-GAN: A Masked Temporal Graph Generative Adversarial Network for Cross-Domain System Log Anomaly Detection	2026	Early Access	Anomaly detection Adaptation models Generative adversarial networks	Anomaly detection of system logs is crucial for the service management of large-scale information systems. Nowadays, log anomaly detection faces two main challenges: 1) capturing evolving temporal dependencies between log events to adaptively tackle with emerging anomaly patterns, 2) and maintaining high detection capabilities across varies data distributions. Existing methods rely heavily on domain-specific data features, making it challenging to handle the heterogeneity and temporal dynamics of log data. This limitation restricts the deployment of anomaly detection systems in practical environments. In this article, a novel framework, Masked Temporal Graph Generative Adversarial Network (MTG-GAN), is proposed for both conventional and cross-domain log anomaly detection. The model enhances the detection capability for emerging abnormal patterns in system log data by introducing an adaptive masking mechanism that combines generative adversarial networks with graph contrastive learning. Additionally, MTG-GAN reduces dependency on specific data distribution and improves model generalization by using diffused graph adjacency information deriving from temporal relevance of event sequence, which can be conducive to improve cross-domain detection performance. Experimental results demonstrate that MTG-GAN outperforms existing methods on multiple real-world datasets in both conventional and cross-domain log anomaly detection.	10.1109/TNSM.2026.3654642
Martine S. Lenders, Carsten Bormann, Thomas C. Schmidt, Matthias Wählisch	A Leaner and Faster Web: How CBOR Can Improve Dynamic Content Encoding in JSON and DNS over HTTPS	2026	Early Access	Internet of Things Encoding Internet	The Internet community has taken major efforts to decrease latency on the World Wide Web with significant improvements in accelerating content transport and in compressing static content. Less attention, however, has been dedicated to compression of dynamic content. Such content is commonly provided by JSON and DNS over HTTPS. Dynamic content objects continue to grow in size, which increases latency and fosters the digital inequality. In this paper, we propose to mitigate this increase by utilizing Concise Binary Object Representation (CBOR), a standard originally designed for the constrained Internet of Things (IoT) to restrict packet sizes and enable efficient encoding of data objects. We provide protocol design and three new data sets for the evaluation of dynamic content, DNS, and the loading of websites. Our key findings are the following: (i) Switching the data representation from JSON to CBOR reduces data by up to 80%. This size reduction can decrease loading times by up to 13.8% when downloading large objects—even in local setups. (ii) Enabling CBOR for DNS over HTTPS (DoH) and DNS over CoAP (DoC) reduces packet sizes significantly. Compressing only names combined with unpacked CBOR achieves maximum gain of 52.2%, using more complex but still lightweight Packed CBOR allows minimizing packets by up to 95.5%. Our lean decoder for name compression can fit into as little as 314 bytes of build size. Our results clearly show the potential of CBOR outside of IoT scenarios. Parts of this research have already influenced work within the IETF.	10.1109/TNSM.2026.3722114
Franck Messaoudi, Luhan Wang, Abdelkader Mekrache, Adlen Ksentini, Bingxuan Li, Jialei Su, Sofiane Messaoudi, Salim El Ghalbzouri	The Brewing Storm in 5G’s Data Plane: Design and Evaluation of a High-Performance eBPF/XDP-Based User Plane Function	2026	Early Access	Quality of service Fluid flow Kernel	This paper presents the design and implementation of a novel 5G UPF leveraging eBPF technology to meet the stringent performance and programmability requirements of emerging 6G systems. Traditional UPF implementations often struggle to balance performance, flexibility, and resource efficiency-challenges particularly critical in CPU- and I/O-constrained edge environments. The proposed eBPF-based UPF architecture mitigates these limitations by embedding core functionalities, such as packet classification, forwarding, and QoS enforcement, directly within the Linux kernel via eBPF programs attached through XDP and tc hook points. Performance evaluation using TRex demonstrates that the proposed solution achieves competitive throughput, low packet loss, and efficient CPU utilization across traffic profiles. Moreover, it maintains full compliance with 5G Core Network standards. Comparative analysis with well-established open-source UPF implementations further underscores its advantages. This work highlights the potential of eBPF as a foundational technology for building next-generation, programmable UPFs optimized for edge cloud deployments in the 6G era.	10.1109/TNSM.2026.3720812
Soonbeom Kwon, Yusu Noh, Youngwoo Jang, Illyoung Choi, Byungchul Tak, In-geol Chun, Young-Kyoon Suh	Scalable and Robust Resource Provisioning via Adaptive Task Scheduling for Edge Devices	2026	Early Access	Schedules Scheduling Cloning	Edge devices, such as wearables, drones, and CCTV systems, are vital for real-time data collection in urban intelligence. However, their limited computational and storage capacities pose significant challenges. While offloading to public clouds offers scalability, it often incurs high latency and operational costs. Conversely, centralizing workloads on edge servers may result in the underutilization of high-performance edge devices. To address these limitations, we introduce ERPF, a Kubernetes-based Edge Resource Provisioning Framework that augments the capabilities of heterogeneous edge environments. ERPF orchestrates dynamic volume provisioning, GPU-aware resource allocation, execution context migration, and adaptive task distribution to improve system flexibility and efficiency. Building on this, we propose a novel adaptive task scheduling technique, termed eATS, composed of three key mechanisms: (i) Partition Smoothing Scheme for stable task granularity control, (ii) Resilient Edge Reintegration for failure detection and task reassignment, and (iii) Competitive Task Cloning for speculative execution with fastest-result commitment. The proposed eATS scheme reduces task execution time by up to 27.6%, lowers partition size variability by 8.7×, and improves scheduling robustness across heterogeneous edge devices over the baseline.	10.1109/TNSM.2026.3694238
Francisco Muro, Eduardo Baena, Tomaso De Cola, Sergio Fortes, Raquel Barco	AI-Driven Optimization of Virtual Network Function Allocation in 6G Non-Terrestrial Networks	2026	Early Access	Resource management Optimization Satellites	The integration of 6G technologies into Non-Terrestrial Networks (NTNs) raises a fundamental orchestration problem: how to allocate Virtual Network Functions (VNFs) across satellite and terrestrial domains under tight onboard resource constraints and a continuously changing topology. The virtualized 6G Open Radio Access Network (O-RAN) paradigm makes it possible to run 5G software stacks on Software-Defined Radios (SDRs) based on General Purpose Processors (GPPs), but it also turns VNF placement into a high-dimensional, multi-objective decision that static heuristics and model-based formulations struggle to capture. This paper addresses that gap by introducing an AI-driven VNF allocation framework for 6G-NTN environments built on an O-RAN-based distributed architecture and orchestrated on top of Kubernetes. The VNF allocation problem is formalized for a multi-domain 6G-NTN scenario with constrained satellite resources, and a measurement-based test campaign is designed to characterize the emulated platform in terms of virtual resource utilization and end-to-end performance. The framework couples tree-based machine learning predictors with a gradient-free optimizer to reach the optimal feasible allocation, outperforming two heuristic baselines drawn from the VNF placement literature by reducing the service RTT by up to 39% and delivering up to 3× higher YouTube DL throughput with respect to the best feasible heuristic. Beyond these gains, the proposed framework establishes a measurement-driven, reproducible methodology for VNF allocation in 6GNTN scenarios, demonstrating that AI-driven orchestration can systematically uncover non-obvious resource configurations that purely analytical or static approaches consistently miss.	10.1109/TNSM.2026.3724474
Wei Sai, Yihui Lu, Xin Guo	A Privacy-Preserving Security Framework for Multi-Party Data Fusion Computing Based on Homomorphic Encryption	2026	Early Access	Security Protocols Information rates	To prevent plaintext exposure in multi-party collaborative computing, this paper proposes a distributed secure multi-party computation protocol based on the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme. Data is encoded and encrypted at the source into CKKS complex polynomial ciphertext, enabling vectorized fusion under shared evaluation keys and threshold decryption in a decentralized architecture without a trusted central authority. Experiments on heterogeneous multi-institution datasets demonstrate low numerical error (9.0×10⁻⁷ at polynomial order 2¹⁶ and depth 12), effective scalability (throughput increasing from 1.12×10⁵ to 1.32×10⁵ ops/s and latency decreasing from 56 ms to 38 ms as nodes scale from 4 to 16), and strong robustness (70% decryption success at a 60% threshold and 95% recovery under malicious interference), showing that the framework achieves efficient computation with strict privacy protection for cross-party data fusion.	10.1109/TNSM.2026.3717343
Deemah H. Tashman, Soumaya Cherkaoui	Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks	2026	Early Access	Reconfigurable intelligent surfaces Reliability Optimization	Cognitive radio networks (CRNs) are a key mechanism for alleviating spectrum scarcity by enabling secondary users (SUs) to opportunistically access licensed frequency bands without harmful interference to primary users (PUs). To address unreliable direct SU links and energy constraints common in next-generation wireless networks, this work introduces an adaptive, energy-aware hybrid reconfigurable intelligent surface (RIS) for underlay multiple-input single-output (MISO) CRNs. Distinct from prior approaches relying on static RIS architectures, our proposed RIS dynamically alternates between passive and active operation modes in real time according to harvested energy availability. We also model our scenario under practical hardware impairments and cascaded fading channels. We formulate and solve a joint transmit beamforming and RIS phase optimization problem via the soft actor-critic (SAC) deep reinforcement learning (DRL) method, leveraging its robustness in continuous and highly dynamic environments. Notably, we conduct the first systematic study of reward poisoning attacks on DRL agents in RIS-enhanced CRNs, and propose a lightweight, real-time defense based on reward clipping and statistical anomaly filtering. Numerical results demonstrate that the SAC-based approach consistently outperforms established DRL base-lines, and that the dynamic hybrid RIS strikes a superior trade-off between throughput and energy consumption compared to fully passive and fully active alternatives. We further show the effectiveness of our defense in maintaining SU performance even under adversarial conditions. Our results advance the practical and secure deployment of RIS-assisted CRNs, and highlight crucial design insights for energy-constrained wireless systems.	10.1109/TNSM.2026.3660728
Abderrahmane Boulahdour, Miloud Bagaa, Adlen Ksentini, Ahmed Ouameur Messaoud, Daniel Massicotte	Towards Software-Defined TSN Scheduling: An eBPF Approach for Stream Processing and Delay Analysis in Industry 5.0	2026	Early Access	Streams Timing Bridges	This paper presents a software-defined Time-Sensitive Networking (TSN) architecture that implements the IEEE 802.1Qcr Asynchronous Traffic Shaper (ATS) using Extended Berkeley Packet Filter (eBPF) technology within Linux-based TSN bridges. By moving traffic shaping logic to the kernel level, our solution eliminates the need for dedicated hardware and enables dynamic, programmable control of frame filtering, metering, and queuing. A Software-Defined Networking (SDN) controller complements the design, providing centralized orchestration of TSN behavior through standardized interfaces and a unified network view. We implement the ATS scheduling model to compute and enforce per-stream eligibility times, supporting time-aware scheduling of concurrent streams within the same priority class. This enables deterministic traffic delivery, which is critical for industrial automation and control. Our approach allows seamless integration into existing infrastructures and aligns with the flexibility objectives of Industry 5.0. Performance evaluations demonstrate accurate scheduling behavior under heterogeneous traffic conditions and quantify the delay introduced by a TSN bridge for multiple coexisting streams.	10.1109/TNSM.2026.3729563
Yali Yuan, Yu Huang, Xingjian Zeng, Hantao Mei, Guang Cheng	M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery	2026	Early Access	Labeling Modeling Electronic mail	The growing complexity of encrypted network traffic presents dual challenges for modern network management: accurate multiclass classification of known applications and reliable discovery of unknown traffic patterns. Although deep learning models show promise in controlled environments, their real-world deployment is hindered by data scarcity, concept drift, and operational constraints. This paper proposes M3S-UPD, a novel Multi-Stage Self-Supervised learning framework for encrypted traffic classification and unknown pattern discovery that synergistically integrates semi-supervised learning with representation analysis. Our approach provides a unified framework for known-class classification and unknown pattern discovery through a four-phase iterative process: 1) probabilistic embedding generation, 2) clustering-based structure discovery, 3) distribution-aligned outlier identification, and 4) confidence-aware model updating. Key innovations include a self-supervised mechanism for unknown pattern discovery that requires neither synthetic samples nor prior knowledge, and a continuous learning framework designed for reliable model updating. Experimental results show that M3S-UPD not only outperforms existing methods on the few-shot encrypted traffic classification task, but also simultaneously achieves competitive performance on the zero-shot unknown pattern discovery task. The code is available at https://github.com/fatmo666/M3S-UPD/.	10.1109/TNSM.2026.3729337
Mubashir Murshed, Glaucio H. S. Carvalho, Robson E. De Grande	Holistic Intelligent Traffic Steering Management in Multi-RAT Vehicular Networks	2026	Early Access	Radio access technologies Rats Vehicles	Multiple Radio Access Technology (multi-RAT) environments provide a promising foundation for service-aware communication in intelligent transportation systems (ITS) and smart cities. However, traffic steering (TS) in highly mobile and ultra-dense vehicular networks remains challenging due to dynamic network conditions, heterogeneous RAT capabilities, varying vehicle requirements, packet loss, latency, and frequent ping-pong RAT switching. In this context, we propose Holistic Intelligent Traffic Steering (HITS), a proactive bi-level TS management framework for multi-RAT vehicular networks. HITS integrates centralized network-wide guidance with local vehicleside decision-making. At the central level, a Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model captures holistic spatio-temporal network dynamics and evaluates RAT optimality. At the local level, a State-Action-Reward- State-Action (SARSA) reinforcement learning agent performs adaptive, vehicle-specific RAT selection using local observations and central-level optimality guidance. Results show that HITS achieves up to 6.5% higher average throughput, reduces packet loss ratio by more than 30.2%, lowers latency by nearly 12.2%, and reduces the ping-pong RAT switching rate by over 24% compared with baseline and state-of-the-art (SoTA) TS approaches.	10.1109/TNSM.2026.3729840
Junior Momo Ziazet, Brigitte Jaumard	Energy Efficient Placement of Logical Functionalities in 5G Networks	2026	Early Access	Energy Copper Modeling	Although 5G networks are more efficient in terms of power consumption to traffic ratio, efforts still need to be made to further increase energy efficiency not only for the radio part, but also with respect to the growing cloud component with edge servers. Consolidation of traffic workloads onto shared infrastructures is a key feature of cloud computing to reduce energy consumption, and logical functionality placement plays a key role in this regard. Here, in the cloud RAN context, we propose a unified and energy-aware logical placement of 5G E2E functionalities, i.e., distributed units (DUs), centralized units (CUs), and user plane functions (UPFs), together with traffic routing. The placement problem is formulated as a large-scale integer linear program and solved using a column generation-based decomposition technique, complemented by an efficient heuristic to ensure tractability and improved scalability. The model captures key network and cloud (compute) resources, jointly optimizing the placement of DU, CU, and UPF components, along with traffic routing, to minimize energy consumption while maintaining low latency and high Quality of Service (QoS). Numerical results, based on an open Montreal traffic dataset, demonstrate that the proposed column generation algorithm achieves near-optimal solutions, while the heuristic approach offers significantly better scalability with consistently strong performance. The proposed methods reduce energy consumption by up to 14% and maintain low-latency service delivery. Furthermore, the results highlight that static, peak-time-based placement strategies can lead to inefficiencies throughout the day, emphasizing the importance of accounting for broader temporal traffic patterns.	10.1109/TNSM.2026.3729149
Pingping Dong, Liying Chen, Xuan Yao, Kai Wang, Lianming Zhang, Jiawei Huang	Fumer: Proactive Time-Shifting for Synchronized Periodic Traffic in Distributed Training	2026	Early Access	Training Timing Modeling	The growth of distributed training models, with parameters now reaching the billion-scale, has shifted the system bottleneck from computation to communication. While Remote Direct Memory Access (RDMA) is widely deployed to improve network performance by circumventing the kernel mechanism, the synchronization-computation cycles under the synchronous parallel mode introduce a highly synchronized and periodic “on-off” bursty traffic pattern, which poses significant challenges to data center networking. Consequently, distributed training suffers from two critical bottlenecks: instantaneous congestion during communication and persistent link idleness during computation. These issues lead to severe bandwidth contention and resource underutilization, ultimately hindering overall training efficiency. To address these challenges, this paper proposes Fumer, a proactive periodic traffic optimization framework that shifts the congestion control paradigm from reactive rate adjustment to proactive time-shifting. Specifically, Fumer leverages In-band Network Telemetry (INT) and Fast Fourier Transform (FFT) with signal-wave separation to decompose interleaved traffic signals, aiming to overcome the lack of periodic awareness. Furthermore, Fumer employs an off-peak transmission optimization algorithm to calculate optimal time-shift values, thereby tackling synchronized congestion and link idleness. By executing proactive off-peak scheduling, Fumer shifts overlapping communication windows into idle periods to smooth traffic peaks in the time domain. Experimental results show that Fumer boosts average path throughput across all workloads to 86.3 Gbps, improving upon DCQCN (42.6 Gbps) by 102.6% and RECC by 22.1%. Furthermore, it reduces the average and 99.9th-percentile iteration times by up to 25.0%-45.4% and 25.5%-49.3%, respectively, demonstrating its efficacy and robustness across diverse large-scale training workloads.	10.1109/TNSM.2026.3728016
Guiyan Liu, Ji Li, Kaixin Qin, Songtao Guo, Liang Liu, Li Yin	AdpVDLTS: Adaptive Spatio-Temporal VNF Placement and Load Balanced Traffic Scheduling in Edge Computing Networks	2026	Early Access	Loading Modeling Timing	The rise of network function virtualization (NFV) technology has enabled virtual network functions (VNF) and service function chains (SFCs) to develop into standard paradigms for service delivery. The uncertain traffic brought about by edge computing has made it a key issue to figure out how to deploy VNFs for network load balancing. However, traditional methods are limited to SFC embedding solutions and resource management and pay less attention to traffic. To address the above issue, this paper takes into account the spatio-temporal characteristics of network traffic and the traffic scheduling after VNF deployment to solve the load balanced VNF deployment problem. We formalize the problem into an NP-hard nonlinear integer programming problem, which will be solved with the proposed algorithm adaptive VNF deployment and load balanced traffic scheduling (AdpVDLTS). AdpVDLTS divides network time into large and small time slots to operate on traffic and VNFs simultaneously, and achieves load balancing through traffic prediction and collaboration with VNF deployment. Compared with the excellent existing algorithms, AdpVDLTS can maintain more stable load balancing, higher throughput, lower deployment cost, and lower latency. In addition, the effectiveness of the traffic prediction algorithm is proved by ablation experiments.	10.1109/TNSM.2026.3729261
Mustafa Türk, Müge Sayıt, Ali C. Begen, Andreas J. Kassler	FROG: Fast Response to Optimization Goals for HTTP Adaptive Streaming over SDN	2026	Early Access	Optimization Videos Servers	In modern video streaming systems, clients typically make independent bitrate decisions without full knowledge of network conditions, often leading to inefficient resource usage and unstable quality. Network-assisted adaptive streaming is rapidly gaining importance in Network and Service Management (NSM), as operators strive to deliver consistently high Quality of Experience (QoE) under dynamic traffic conditions. This paper introduces FROG, a novel, real-time Software-Defined Networking (SDN)-assisted framework designed to coordinate multi-server and multipath HTTP Adaptive Streaming (HAS) using standardized metrics carriage: Common Media Client Data (CMCD) and Common Media Server Data (CMSD). FROG employs an innovative two-stage optimization workflow in which an initial Linear Programming (LP) model rapidly determines feasible bandwidth bounds, server selection, and path capacities, thereby transforming the remaining optimization into a sequential layer-selection process for coordinated quality selection and flow allocation. This decomposition enables sub-second optimization at the scale of thousands of users, while buffer-aware client feedback is integrated to proactively prevent stalls and maintain system stability. Experiments on an emulated SDN testbed demonstrate that FROG achieves QoE comparable to that of the optimal MILP solution on tractable instances while virtually eliminating video stalls, and significantly reduces quality oscillations, outperforming state-of-the-art network-assisted approaches by up to 2.32× under playback-driven evaluation scenarios. Scalability experiments with up to 4,000 clients further demonstrate sub-second optimization runtimes, confirming the practicality of FROG for large-scale deployments.	10.1109/TNSM.2026.3729597
Yuqiang Wen, K. L. Eddie Law	Selected Group Broadcasting: Modeling and Applications	2026	Early Access	Modeling Broadcasting Timing	Modern decentralized applications frequently employ one-to-many messaging over dynamic recipient groups, a paradigm we term Selected Group Broadcasting (SGB). Though one-to-one traffic models (e.g., M/M/1 queue) are well studied, traffic characterization for one-to-many connectivity remains underdeveloped. In this paper, we investigate the delay distribution of one-to-many traffic over an SGB in a wide-area communication system. Depending on the underlying network input, the SGB problem naturally decomposes into two distinct paradigms: SGBs (for Sequential inputs) and SGBp (for Parallel inputs). Asymptotic analysis reveals that SGBs and SGBp are governed by fundamentally different stochastic dynamics, necessitating dedicated modeling approaches for each. This paper focuses exclusively on the SGBs paradigm, providing a rigorous analytical treatment and establishing an operatortheoretic performance framework. For first-order statistics of SGBs, we derive exact closed-form solutions under exponential inter-ACK interval (ACK Interval) inputs. By exploiting the rational Laplace-domain structure of the forward operator, we extend the model to precisely compute output distributions for any non-negative distribution input. Furthermore, we introduce an equivalent SGBs analysis scheme via the inverse operator, enabling the transformation of continuous commit-time distributions into physically interpretable ACK Interval profiles. For second-order statistics, we establish a formalized functional hypothesis under exponential ACK Intervals, validated via simulation. We verify the derived probability density functions with simulated delays; the results are confirmed via Kolmogorov-Smirnov tests.	10.1109/TNSM.2026.3729017
Yuya Miyaoka, Masaki Inoue, Kengo Urata, Shigeaki Harada	Chat-Driven Optimal Management for Virtual Network Services	2026	Early Access	Modeling Large language models Central Processing Unit	This paper proposes a chat-driven network management framework that integrates natural language processing (NLP) with optimization-based virtual network allocation, enabling intuitive and reliable reconfiguration of virtual network services. Conventional intent-based networking (IBN) methods depend on statistical language models to interpret user intent, but cannot guarantee the feasibility of generated configurations. To overcome this, we develop a two-stage framework consisting of an Interpreter, which extracts intent from natural language prompts using NLP, and an Optimizer, which computes feasible virtual machine (VM) placement and routing via integer linear programming. In particular, the Interpreter translates user chats into update directions, i.e., whether to increase, decrease, or maintain parameters such as CPU demand and latency bounds, thereby enabling iterative refinement of the network configuration. In this paper, two distinct Interpreter implementations are introduced: a Sentence-BERT model with support vector machine (SVM) classifiers and a large language model (LLM). Experiments in single-user and multi-user settings show that the framework dynamically updates VM placement and routing while preserving feasibility. The LLM-based approach achieves higher accuracy with fewer labeled samples, whereas the Sentence-BERT with SVM classifiers provides significantly lower latency suitable for real-time operation. We also compare our cascade structure method with an end-to-end LLM approach, highlighting our proposed method’s high level of reliability.	10.1109/TNSM.2026.3726950
Raeed Al-Sabri, Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha	DMGCRL: Dynamic Multi-Scale Graph Contrastive Representation Learning For Network Intrusion Detection	2026	Early Access	Modeling Intrusion detection Labeling	Graph neural networks (GNNs) have recently attracted significant attention in network intrusion detection systems (NIDS) due to their ability to model network traffic as graphs and capture complex relationships within network flows. However, existing GNN-based methods face critical limitations: they rely on limited or noisy labeled data and struggle to detect threats at various scales, ranging from local anomalies (e.g., port scanning) to coordinated subnetwork attacks (e.g., botnets) and global network-wide campaigns (e.g., DDoS attacks). To bridge this gap, we propose Dynamic Multiscale Graph Contrastive Representation Learning (DMGCRL), a self-supervised framework that hierarchically models network intrusions at different levels. At the node level, DMGCRL constructs structure-aware subnetworks around individual traffic flows to capture fine-grained behavioral deviations. For subnetwork-level threats, it employs substructure-aware pooling to identify coordinated anomalies among clustered malicious nodes. Finally, at the global level, DMGCRL derives representations that reflect the holistic state of the network, enabling detection of large-scale threats, such as distributed malware propagation. DMGCRL designs a shared GNN encoder with a multi-level contrastive loss to align multiscale representations while largely eliminating label dependence. It learns discriminative features from unlabeled traffic, refines decision boundaries without supervision, and reveals anomalies by contrasting related and unrelated nodes across scales. Performance evaluation was conducted on five publicly available network traffic datasets for binary and multiclass detection. Results show that DMGCRL consistently outperforms SOTA methods, achieving an F1 score of 99.86% on NF-CSECIC-IDS2018-V2 and 96.11% on NF-UNSW-NB15-V2 under binary detection and the lowest mean false positive rates, 1.28% and 2.33% under multiclass detection on the two datasets.	10.1109/TNSM.2026.3726282
Rita Ingabire, Antonio Bazco-Nogueras, Vincenzo Mancuso, Luis M. Contreras, Jesús Folgueira	Explainable AI to Understand the Latency Behavior of Public Cloud Service Platforms	2026	Early Access		Cloud platforms have become a core component of the Internet because most services and products rely on them to host their backends. Estimating and understanding the latency experienced when accessing those cloud platforms is a challenge of growing importance that has not been sufficiently studied. To address this relevant matter, we conducted a three-month measurement campaign, collecting traceroute data every 30 min across 256 source–destination probe pairs. Our specific goal is to analyze whether the current network is able to provide adequate performance for emerging applications and services. We use this dataset to evaluate the performance of forecasting algorithms when predicting cloud latency from both temporal and spatial perspectives, and we leverage post-hoc explainability methods to identify the drivers affecting latency. Several prior studies provide public cloud-latency datasets, but these datasets are generally analyzed in isolation. To close this gap and provide a cross-dataset comparative analysis of cloud-latency measurements, we analyzed the related publicly available datasets and applied a common forecasting and explainability workflow to compare their findings. Our analysis reveals that operators do not require complex methods to predict latency and that distance and a few other simple features are sufficient to achieve operationally accurate predictions. We find latency to be remarkably stable from the user’s perspective, both over the duration of the campaign and across hours of the day, which contrasts with previous findings, and we show that the specific path traversed has a significant impact on latency.	10.1109/TNSM.2026.3730351
Nicola D’Ambra, Ferdinando Marrone, Pasquale Imputato, Stefano Avallone	Contention Window Tuning for Multi-Link Networks using Reinforcement Learning	2026	Early Access		The increasing demand for higher throughput and lower latency in modern applications has driven the evolution of IEEE 802.11 with the introduction ofWi-Fi 7. The new Extremely High Throughput (EHT) amendment enhances performance with Multi-Link Operation (MLO), which allows Wi-Fi stations equipped with multiple radios to transmit and receive concurrently over different frequency links, improving channel access opportunities, reducing latency, and enhancing overall throughput and reliability. However, in scenarios with irregular or high traffic loads across links, MLO can degrade performance due to contention imbalance and inefficient medium access coordination. In particular, TCP flows are highly sensitive to such conditions, as increased contention and collisions can trigger congestion control mechanisms that reduce sending rates and impair throughput. This work investigates the impact of MLO on TCP traffic and proposes a Reinforcement Learning (RL)-based contention control strategy to improve load distribution in congested multi-link environments. The RL-based method dynamically adapts the channel access aggressiveness of stations according to network conditions, mitigating contention imbalances across links and achieving TCP performance improvements while maintaining or, in some cases, improving UDP performance. Simulation results show that the proposed AI-driven contention control provides a flexible and effective solution for traffic balancing in multi-link Wi-Fi 7 networks.	10.1109/TNSM.2026.3730647
Stephen Jasina, Loqman Salamatian, Joshua Mathews, Scott Anderson, Paul Barford, Mark Crovella, Walter Willinger	Matisse: Visualizing Measured Internet Latencies as Manifolds	2026	Early Access		Manifolds are complex topological spaces that can be used to represent datasets of real-world measurements. Visualizing such manifolds can help with illustrating their topological characteristics (e.g., curvature) and providing insights into important properties of the underlying data (e.g., anomalies in the measurements). In this paper, we describe a new methodology and system for generating and visualizing manifolds that are inferred from actual Internet latency measurements between different cities and are projected over a 2D Euclidean space (e.g., a geographic map). Our method leverages a series of graphs that capture critical information contained in the data, including well-defined locations (for vertices) and Ricci curvature information (for edges). Our visualization approach then generates a curved surface (manifold) in which (a) geographical locations of vertices are maintained and (b) the Ricci curvature values of the graph edges determine the curvature properties of the manifold. The resulting manifold highlights areas of critical connectivity and defines an instance of “Internet delay space” where latency measurements manifest as geodesics. We describe details of our method and its implementation in a tool, which we call Matisse, for generating, visualizing and manipulating manifolds projected onto a base map. We illustrate Matisse with three case studies: a simple example to demonstrate key concepts, and visualizations of the US and Europe public Internet to show Matisse’s utility.	10.1109/TNSM.2026.3730274