TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
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		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
Gianluca Reali, Mauro Femminella	Serverless Function Latency Model in Edge Computing	2026	Early Access	Modeling Timing Service level agreements	This paper illustrates a statistical model of the service latency of serverless functions, with particular reference to edge computing. Given a set of functions deployed in an edge computing system, this model can be very useful for several reasons. First of all, evaluating the quality of the user experience is essential to promptly assess whether the system configuration state requires corrective interventions. In case of malfunctions, the model may highlight performance problems. The model can also identify possible Service Level Agreement violations in real time, before they can cause operational problems. In addition, it allows balancing cost-performance trade-offs. The proposed model is based on the observation of experimental latency values and is adapted to produce a statistical distribution that closely approximates the real one. Furthermore, it can be exploited synergistically with Machine Learning algorithms, to dynamically optimize serverless systems in edge computing. We have evaluated the performance of the model by using latency samples generated ad hoc through a mixture of distributions, including long tailed ones, and by using the well-known Azure Functions and Globus datasets. The experimental results show a significant closeness of the statistical distribution of the generated data with the experimental ones, up to high percentiles.	10.1109/TNSM.2026.3725364
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
Martin Trullenque Ortiz, Daniel Camps Mur, Oriol Sallent, Jad Nasreddine	FOM-5G: A Learning-Based Framework for Overload Mitigation in V2X-Enabled 5G Networks	2026	Early Access	5G mobile communication Cells (biology) Vehicle-to-everything	Public networks deploying 5G-Advanced technology are expected to support large-scale connected and cooperative vehicular services. To this end, 3GPP has enabled the deployment of Day 1 V2X safety services over public cellular networks, which provide the global coverage and low latencies these services require. However, high vehicular mobility may result in traffic jams that strain radio resources, leading to cell overload situations that jeopardize the delivery of V2X services. Previous studies have shown that the best action to mitigate cell overloads strongly depends on road topology and the location of the traffic jam with respect to the cell geometry. Moreover, a fundamental insight to address V2X related cell overloads is to realize that the majority of traffic jams are repeatable, hence historical information should be used to take radio resource management (RRM) decisions. In this paper, we present a novel framework for cell overload mitigation in 5G networks (FOM-5G), an RRM solution for ORAN-based public 5G networks that leverages historical information to prevent recurring cell overloads, particularly those affecting V2X services. We validated FOM-5G through comprehensive simulations incorporating three real vehicular mobility patterns from a European city experiencing traffic congestion. The results show that FOM-5G can adapt its congestion control actions to varying traffic conditions, effectively exploit historical data, and achieve superior performance compared to static congestion mitigation approaches.	10.1109/TNSM.2026.3725207
Qiru Chen, Xinping Guan, Lei Xu, Yanzhou Zhang, Qimin Xu, Cailian Chen	Knowledge-Aware Schedulability Analysis for Time-Sensitive Networking: A GNN-Based Method	2026	Early Access	Modeling Fluid flow Schedules	Industrial automation is rapidly evolving toward flexible production. This transition requires networks to ensure the deterministic transmission of varying traffic sets across different production stages. Consequently, the system must be capable of rapidly analyzing whether fixed network resources can accommodate all service requirements prior to actual scheduling. While Time-Sensitive Networking (TSN) provides the deterministic transmission for such environments, existing schedulability assessments rely on exhaustive scheduling tests. However, the scheduling process is inherently an NP-hard constraint satisfaction problem, whose heavy computational overhead severely limits deployment agility. Therefore, it is critical to develop a method that can rapidly predict the constraint satisfiability of diverse traffic sets without repetitive and time-consuming scheduling. In this work, given the inherent graph-structured nature of network infrastructure and traffic patterns, we design a graph neural network model to explicitly capture complex spatial dependencies. As node attributes, sparse basic traffic features are distilled as expert knowledge and integrated, thereby enhancing prediction accuracy. When a traffic set is deemed unschedulable, we explore the traffic features and links with the greatest impact. Based on this, a feature-driven rerouting strategy is proposed to find a more schedulable traffic behavior. The evaluation results show that the model demonstrates the capability to process thousands of datasets within hundreds of microseconds, while guaranteeing a prediction accuracy of over 90% and an increase in the count in schedulable flows by about 25% compared to the standard Dijkstra’s shortest path algorithm baseline.	10.1109/TNSM.2026.3724835
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
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
Nan Wei, Sizhe Huang, Lihua Yin, Ziying Zhu, Wenting Wang	Correction Forest: A Misclassification Correction model for Reducing the Total Error Rate of IIoT Network Intrusion Detection	2026	Early Access	Modeling Industrial Internet of Things Internet of Things	Deep learning-based network intrusion detection systems (NIDSs) in the Industrial Internet of things (IIoT) are inevitably prone to producing misclassified samples. Correction models can identify and correct these samples to reduce the total error rate (TER) of NIDSs. Existing correction models fail to account for the uneven distribution of misclassified samples in the prediction intervals of NIDSs due to IIoT data imbalance, re-sulting in over-correction and increased TERs. Given this, we propose a novel correction model called Correction Forest for correcting the misclassified samples of NIDSs targeting imbalanced IIoT network traffic dataset. Correction Forest adopts a generation-correction strategy. The generation process divides the output values of NIDSs with imbalanced dataset into fine-grained bins, and then generates novel misclassification features for each bin using a balanced hybrid Random Forest. The correction process calculates feature importance score for misclassification features and corrects misclassification samples by a K-Nearest Neighbors (KNN) -based algorithm. Evaluated on 15 imbalanced IIoT datasets with varying malicious sample ratios, Correction Forest significantly outperforms 4 state-of-the-art models. Under the 1.25% setting of NSL-KDD, Correction Forest improves F1-Score from 0.3144 to 0.7345, an absolute gain of 0.4201. On the TON_IoT at 12.5%, it achieves a maximum R.TER of 0.3750 among the state-of-the-art models.	10.1109/TNSM.2026.3724323
Elham Amini, Jelena Mišić, Vojislav B. Mišić	Deadline-Aware SRPT Scheduling for Paxos Consensus	2026	Early Access	Schedules Scheduling Timing	Paxos consensus protocol is widely used in distributed systems, yet their performance can degrade under heterogeneous workloads and deadline-constrained requests. Traditional priority-based Paxos extensions rely on static scheduling policies that are unable to adapt to dynamically changing urgency. This paper proposes a deadline-aware scheduling framework for Paxos based on the Shortest Remaining Processing Time (SRPT) discipline. The proposed approach dynamically prioritizes requests according to their expected completion behavior and their likelihood of meeting assigned deadlines, enabling preemptive scheduling decisions at the consensus leader. An analytical queueing model is developed to characterize the mean waiting time, mean residence time, and mean response time under SRPT scheduling. Using these results, a probabilistic measure of deadline satisfaction is derived and employed to guide scheduling decisions. Analytical and numerical evaluations demonstrate that the proposed framework significantly improves deadline satisfaction while preserving the delay-optimal properties of SRPT, making it well suited for latency-sensitive Paxos deployments.	10.1109/TNSM.2026.3724325
Ping He, Yu Yao, Xu Li, Yao Hu, Wei Yang	FIGAN: Diversity-Oriented Traffic Generation for Industrial Protocol Format Inference	2026	Early Access	Protocols Modeling Fuzzing	Protocol Format Inference is a pivotal step in the reverse engineering of proprietary protocols, yet its effectiveness is constrained by the scarcity of high-quality training data. In industrial control systems, the rigid and cyclical nature of traffic results in a "long-tail" distribution, where diverse functional scenarios are severely underrepresented. Existing generative approaches, primarily designed for fuzzing or intrusion detection, fail to resolve the intrinsic conflict between syntactic validity and semantic diversity required for protocol format inference. To bridge this gap, we propose FIGAN, a stage-wise decoupled generative framework tailored to synthesize high-fidelity traffic for protocol format inference. By isolating flexible distribution learning from rigid syntax enforcement, FIGAN liberates the generative process to extrapolate novel payload variations from a continuous latent space, effectively surmounting the limitations of sparse seed data. Specifically, the framework integrates three synergistic modules: first, heuristic pre-processing that constructs semantic templates as a prior knowledge base; second, a generative adversarial architecture optimized via discrete relaxation to explore high-dimensional payload patterns independently of syntax rules; and finally, a closed-loop verification mechanism that performs syntactic calibration and functional validation against simulated device responses. Evaluations on four real-world protocols (Modbus TCP, S7Comm, Omron FINS, and DNP3) demonstrate that FIGAN significantly outperforms state-of-the-art baselines. The source code has been open-sourced https://github.com/MissHP111/FIGAN.	10.1109/TNSM.2026.3717268
Xiaoxuan Hu, Xiaochen Dong, Zhenjiang Dong, Yanfei Sun, Yufeng Guo, Jin Qi	A dynamic DAG-Based Scheduling Strategy for Cross-Chain Transaction Management	2026	Early Access		Cross-chain sharding improves interoperability and scalability, yet relay-chain execution remains constrained by dynamic dependencies and shard contention. This paper characterizes the relay-chain execution mechanism of cross-chain sharded transactions and models registration, recording, and confirmation transactions as a dynamic DAG, so that scheduling decisions respect the prerequisite dependencies required by the underlying cross-chain atomicity and consistency mechanisms. On this basis, we propose Fast Out-degree based Monte Carlo Tree Search (FO-MCTS), a dynamic DAG scheduling algorithm for cross-chain sharding. FO-MCTS uses dependency relationships to guide executable-node priority and shard-search expansion, enabling dependency-continuous scheduling while avoiding exhaustive shard-assignment search. Simulation results show that FO-MCTS improves transaction throughput by about 25% on average, reduces the cross-shard communication ratio by 65%–85%, and maintains lower scheduling-induced delay.	10.1109/TNSM.2026.3726301
Akhilesh Patel, Yatindra Nath Singh	Scalability and Delay Analysis of XR Traffic in Optical Access Networks	2026	Early Access	Extended reality Modeling Delays	The rapid evolution of 5G and emerging 6G networks requires optical access systems to support immersive extended reality (XR) services with stringent quality-of-service (QoS) requirements, like ultra-low latency and high bandwidth. However, conventional dynamic bandwidth allocation (DBA) schemes in passive optical networks (PONs) allocate upstream bandwidth solely based on reported queue occupancy, without considering the unique characteristics of XR traffic. To address these limitations, we propose an XR-aware Predictive (XP)-DBA scheme that integrates XR traffic prediction, deadline-aware scheduling, adaptive grant control, and a cycle-controller to proactively allocate bandwidth, prioritize latency-critical packets, and limit polling-cycle growth. We also derive closed-form analytical expressions to characterize XR-specific stability and delay feasibility in PON systems. We evaluate XP-DBA under standardized and burst-enhanced XR traffic models across varying XR user densities and transmission distances of up to 100 km. The results show that XP-DBA will reduce latency, jitter, and polling-cycle time while increasing throughput and supporting higher XR user densities under heavy network loads without violating XR delay bounds. These findings establish XP-DBA as an efficient and scalable scheduling solution for next-generation immersive XR services over long-reach optical access networks.	10.1109/TNSM.2026.3725139
Lin Cong, Junru Cai, Ying Wang, Peng Yu, Xuesong Qiu, Shaoyong Guo, Ao Xiong	Dynamic E2E Channel Orchestration in Metro Transport Network	2026	Early Access	Algorithms Optimization Timing	In the era of 5G and beyond, the hard-isolated channels enabled by time slot cross-connects in metro transport network (MTN) effectively meet the demands of emerging network services for low latency, low jitter, flexible bandwidth, and secure isolation. However, the dynamic arrival and departure of tenant virtual network request (VNRs) lead to resource fragmentation within the MTN transport network, resulting in inefficient resource utilization. To mitigate network resource fragmentation, we formulate the MTN dynamic channel orchestration problem and propose a fragmentation-aware MTN dynamic channel orchestration method. This method comprises two key components: a greedy graph-reconstruction-based channel mapping algorithm and a fragmentation-aware channel reconfiguration algorithm. The former optimizes MTN channel resource allocation to achieve static channel orchestration, while the latter, leveraging a simulated annealing-based channel reconfiguration strategy, dynamically adjusts channel allocations based on the static orchestration results, thereby reducing fragmentation levels. Compared to existing approaches, under varying network load conditions, the proposed channel mapping algorithm reduces the consumption of network time slot resources by 16.3% -20.6%, while the channel reconfiguration algorithm significantly lowers the levels of network fragmentation by 48.7% -79.5%, and reduces the running time by 91.3%–94.2% compared with the baseline.	10.1109/TNSM.2026.3723823
Didik Sudyana, Wong Yu Xuan, Laurens D’hooge, Ren-Hung Hwang, Narn-Yih Lee, Pei-Yin Chen, Tim Wauters, Bruno Volckaert, Filip De Turck	Bridging Training–Deployment Gap in Intrusion Detection with Source-Free Domain Adaptation	2026	Early Access	Modeling Internet of Things Fluid flow	Machine learning (ML)–based intrusion detection systems (IDS) frequently degrade when deployed across heterogeneous networks due to domain shifts in traffic composition and monitoring configurations. Conventional domain adaptation (DA) methods mitigate this issue by aligning source and target distributions, but they often rely on retaining source-domain data at deployment—an impractical requirement that undermines operational scalability and reusability. To address this gap, we propose TRANSFA-IDS (Transformer Source-Free Adaptation for IDS), a lightweight source-free adaptation framework that recalibrates a source-trained IDS using only target traffic data. TRANSFA-IDS converts tabular flow records into structured RGB image embeddings and employs a compact Vision Transformer with a Deep Support Vector Data Description (Deep-SVDD) head to learn transferable normal representations. At deployment, adaptation is performed by fine-tuning only the last transformer block on a small target buffer, realigning target representations without retraining or access to source data. Experiments on cross-dataset transfer between CIC-IDS-2018 and UNSW-NB15 show that TRANSFA-IDS achieves AUROC of 0.9177 and 0.9071 in the two transfer directions, reduces target-domain benign false positives by over 60% relative to the same source-pretrained model deployed without source-free adaptation, and adapts substantially faster than supervised and unsupervised DA baselines while using at most 20% of the target-domain data. These results indicate that source-free adaptation can achieve both strong detection performance and a practical deployment-oriented design, with cross-benchmark evidence of scalable adaptation across heterogeneous network environments.	10.1109/TNSM.2026.3723866
Xili Wan, Fuliang Yang, Xinjie Guan, Zuwei Zhang, Yihan Ji	Joint Dataflow and In-Network Computing Resource Optimization for Dynamic LEO Satellite Systems	2026	Early Access	Satellites Information rates Throughput	Low Earth Orbit (LEO) satellite constellations are emerging as an important platform for distributed dataflow execution in space-terrestrial integrated networks. Existing studies largely treat routing and processing separately, while next-generation LEO systems are expected to process and transform data in transit by leveraging on-board computing and software-defined infrastructures. However, jointly optimizing routing and in-network processing in dynamic LEO satellite networks remains challenging because of time-varying connectivity, limited on-board resources, and bandwidth constraints. In this paper, we formulate the Dynamic LEO In-network Processing Dataflow Optimization (DLIDO) problem, which aims to maximize the throughput of processed dataflows by jointly optimizing routing paths and processing-resource allocation over a dynamic flow network. We present an approximation algorithm with a proven (1−ϵ) approximation guarantee for 0 < ϵ ≤ 0.5, providing near-optimal throughput under dynamic processing and communication constraints. To further improve efficiency and practicality, we develop a 2-walk based iterative heuristic algorithm that substantially reduces runtime while maintaining strong empirical performance, and in some regimes provably optimal behavior. Extensive evaluations on realistic LEO network topologies show that both algorithms significantly outperform existing approaches in throughput and adaptability, highlighting a promising direction for dataflow-aware scheduling and optimization in dynamic satellite systems.	10.1109/TNSM.2026.3722902
Yuyu Zhao, Siyuan Zhou, Guang Cheng, Yuyang Zhou, Zihan Chen, Wei Zhang	CPRANT: Towards a Computing Power Network Telemetry Architecture Based on Collaborative SRv6 and FPGA Optimization	2026	Early Access	Telemetry Planing Field programmable gate arrays	The emergence of Computing Power Networks (CPN) as critical AI infrastructure poses a key networking challenge: how to obtain visibility across the network into computing and transport resources in real time while minimizing the telemetry overhead on user traffic and control/forwarding devices. This paper introduces CPRANT (Computing Power Networks Telemetry based on SRv6 and FPGA), an FPGA-based plug and play In-Band Network Telemetry (INT) framework that addresses this challenge through a joint hardware and software design. The core innovation lies in using FPGA spatial parallelism to establish physically isolated processing paths for telemetry tasks and forwarding tasks, achieving line rate packet processing with a verified CPN’s forwarding latency of only 15 ns. We propose a novel INT mechanism based on SRv6 that eliminates linear metadata accumulation at each hop in traditional INT via SID reconstruction and time division multiplexing, reusing native SRv6 header space and preserving forwarding compatibility. CPRANT further incorporates adaptive sampling and redundant path elimination algorithms to dynamically optimize telemetry processes, demonstrating a 52.4% reduction in bandwidth and a 45.9% reduction in the load of the control plane. Experimental validation confirms the operational efficiency of CPRANT, maintaining high information fidelity (effective telemetry information yield > 0.85) and comprehensive coverage (path overlap < 4%) with a lightweight resource footprint (< 60 MB). This solution provides a scalable telemetry paradigm for mission critical CPN applications, particularly in large scale AI deployment scenarios requiring monitoring that does not disrupt services.	10.1109/TNSM.2026.3722680
Nguyen Quang Hieu, Dinh Thai Hoang, Diep N. Nguyen, Mohammad Abu Alsheikh, Carlos C. N. Kuhn, Yibeltal F. Alem, Ibrahim Radwan	End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systems	2026	Early Access	Receivers OFDM Quantization (signal)	Full 3D human pose reconstruction is a critical enabler for extended reality (XR) applications in future sixth generation (6G) networks, supporting immersive interactions in gaming, virtual meetings, and remote collaboration. However, achieving accurate pose reconstruction over wireless networks remains challenging due to channel impairments, bit errors, and quantization effects. Existing approaches often assume error-free transmission in indoor settings, limiting their applicability to real-world scenarios. To address these challenges, we propose a novel deep learning-based framework for human pose reconstruction over orthogonal frequency-division multiplexing (OFDM) systems. The framework introduces a two-stage deep learning receiver: the first stage jointly estimates the wireless channel and decodes OFDM symbols, and the second stage maps the received sensor signals to full 3D body poses. Simulation results demonstrate that the proposed neural receiver reduces bit error rate (BER), thus gaining a 5 dB gap at 10−4 BER, compared to the baseline method that employs separate signal detection steps, i.e., least squares channel estimation and linear minimum mean square error equalization. Additionally, our empirical findings show that 8-bit quantization is sufficient for accurate pose reconstruction, achieving a mean squared error of 5×10−4 for reconstructed sensor signals, and reducing joint angular error by 40% for the reconstructed human poses compared to the baseline. The practical feasibility of our framework is demonstrated through validation in an open-source ray tracing simulator incorporating realistic 3D scenes and human models.	10.1109/TNSM.2026.3722545
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
Depeng Xu, Guozhen Cheng, Hongchao Hu, Quan Ren, Xiaohan Yang, Kangxu Wang	STNet: Multi-Scale Spatiotemporal Learning and Adaptive Fusion for Few-Shot Tor Traffic Classification	2026	Early Access	Modules (abstract algebra) Modeling Accuracy	The Tor network’s anonymity is increasingly exploited for cybercrime, creating a demand for accurate traffic classification under strict few-shot constraints. While recent efforts like WF-Transformer demonstrate strong temporal modeling capabilities, they still require abundant labeled data and struggle to generalize under defense-induced distortions and open-world unknown traffic. To address these gaps, we propose STNet (SpatioTemporal Multi-scale Augmentation and fusion Network), an episode-based few-shot learning architecture for Tor traffic classification. Unlike simple module stacking, STNet adopts a modular decoupling design: (1) a Multi-Scale Spatiotemporal Feature Fusion (MSMF) module captures packet-level and flow-level patterns to resist obfuscation; (2) scenario-adaptive modules tackle domain shifts in closed-world settings and feature scarcity in open-world settings; and (3) a Hierarchical Layer Attention (HLA) mechanism dynamically fuses heterogeneous features from different deployment positions. Extensive experiments on real-world Tor traffic show that STNet consistently outperforms representative baselines including WF-Transformer. In closed-world settings, it limits the accuracy drop under WalkieTalkie obfuscation to 13.6 percentage points. In open-world 10-shot evaluation, it achieves 92.1% AU-COVR and 79.1% unknown-class F1-score, surpassing the best baseline by 4.9 and 6.0 percentage points, respectively. These results demonstrate the effectiveness of decoupling universal feature extraction from scenario-specific adaptation in few-shot Tor traffic analysis.	10.1109/TNSM.2026.3722541
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