TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
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
Shengye Gong, Hui Yang, Qiuyan Yao, Xin Li, Tiankuo Yu, Wenxin Liu, Yang Zhao, Yucong Liu, Jie Zhang, Mohamed Cheriet	Resilient Hierarchical Polyhedron-based Configuration Protection for Large-Scale Multi-Domain Optical Networks	2026	Early Access	High performance computing Joining processes Protection	Large-scale multi-domain optical networks are increasingly exposed to spatially correlated and concurrent failures, while monolithic pre-configured protection structures incur rapidly increasing global search complexity and protection-resource costs under heterogeneous physical constraints. To address this challenge, this paper proposes a resilient Hierarchical Polyhedron-based Configuration (HPC) architecture that decomposes a monolithic global survivability requirement into adaptive intra-domain connectivity constraints and a compact inter-domain connectivity constraint. To alleviate resource imbalance caused by topology-only network partitioning, a Topology-Resource Aware Domain Partitioning (TRADP) algorithm is developed to construct protection-supportable sub-domains by jointly considering topological structure and physical resource distribution. To avoid imposing a rigid protection level on heterogeneous domains, HPC assigns adaptive intra-domain connectivity targets according to local failure exposure and physical resource supportability. To reduce the complexity of cross-domain protection while preserving physical feasibility, a super-node abstraction and physical bridge-link mapping mechanism is employed to construct the inter-domain protection backbone under capacity, spectrum, and SRLG constraints. Extensive evaluations on networks of up to 1000 nodes and representative GEANT and NSFNET topologies demonstrate that HPC-TRADP maintains a recovery rate of 92.94% at 1000 nodes and 85.82% even when 30% of physical links fail concurrently. It further reduces protection overhead by up to 79.60% relative to p-KECS while maintaining efficient recovery paths, demonstrating a favorable balance between survivability, resource efficiency, and scalability.	10.1109/TNSM.2026.3750417
Mohammad Saberi, Ali Ahmadi Esfidi, Mahdi Dolati, Naeimeh Omidvar	FlexINA: In-Network Aggregation for Accelerating Distributed Machine Learning with Flexible Routing	2026	Early Access	Modeling Switches Timing	The growing scale and complexity of modern machine learning (ML) models have rendered distributed training indispensable. However, as model dimensions expand, communication overhead increasingly dominates the overall training time, creating a critical performance bottleneck. While techniques such as gradient compression offer partial relief, they often incur substantial computational overhead or require specialized hardware. In-network aggregation (INA) has emerged as a compelling alternative, offloading partial computations—such as gradient reduction—directly onto programmable network switches. Yet, current INA frameworks predominantly rely on rigid, static routing schemes or uncoordinated, opportunistic aggregation, fundamentally limiting their capacity to exploit the full potential of programmable data planes. To address this limitation, we formulate the Joint Scheduling-Routing for In-Network Aggregation (JINA) problem. JINA introduces a time-slotted execution model to enable fine-grained temporal scheduling synchronously with spatial routing. By leveraging source routing, it dictates perpacket forwarding paths while explicitly coordinating multi-hop partial aggregation across the network. Furthermore, we propose FlexINA, a scalable optimization framework that solves the JINA problem through a three-phase dimensionality reduction strategy: host-level pre-aggregation, topological switch filtration, and temporal window partitioning. Extensive evaluations across fifteen diverse network topologies demonstrate that FlexINA reduces the total network load by up to 8.3% compared to INA baselines, while maintaining high computational efficiency.	10.1109/TNSM.2026.3740503
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
Mario Chahoud, Hani Sami, Ahmad Hammoud, Rabeb Mizouni, Hadi Otrok, Jamal Bentahar, Azzam Mourad, Chamseddine Talhi	Multi-agent Deep Reinforcement Learning Scheme for On-Demand Federated Learning	2026	Early Access	Modeling Federated learning Training	The rapid growth of intelligent IoT devices and mobile edge infrastructures has generated massive decentralized data, necessitating efficient and privacy-preserving learning. Federated Learning (FL) enables distributed model training without sharing raw data; however, its performance degrades in dynamic mobile environments due to unpredictable availability, stragglers, and frequent dropouts. In On-Demand FL, training services and models are deployed only when needed rather than relying on always-active infrastructure, requiring orchestrators to adapt to client arrivals, departures, and mobility while efficiently allocating limited resources and maintaining training continuity. Existing client-selection methods rely on static heuristics or centralized Reinforcement Learning models that struggle to scale and adapt to such dynamics. To address these challenges, we propose a cooperative Multi-Agent Deep Reinforcement Learning (MADRL) framework for client selection in On-Demand FL. Multiple orchestrators act as agents under a Centralized Training and Decentralized Execution (CTDE) paradigm. The problem is formulated as a Partially Observable Markov Game (POMG), capturing mobility, partial observability, and distributed decision-making. A mobility-aware reward integrates accuracy, resource efficiency, and a Useful Handoff Bonus to preserve learning continuity. Containerization further enhances adaptability. Experiments demonstrate faster convergence, improved resource utilization, and enhanced robustness under real mobility traces.	10.1109/TNSM.2026.3750310
Shyam Kumar Shrestha, Shiva Raj Pokhrel, Jonathan Kua	Adapting Large Language Models for TCP Fairness over Wi-Fi	2026	Early Access	Large language models TCP Modeling	Designing Transmission Control Protocol (TCP) Congestion Control Algorithms (CCAs) for heterogeneous Wi-Fi networks remains a challenge due to rapidly varying delay, loss, and contention dynamics. These effects often lead to CCA incompatibility, flow unfairness, and starvation among competing TCP flows. Although Deep Reinforcement Learning (DRL) has shown promise in mitigating these challenges, its slow convergence, high training costs, and limited generalization hinder practical deployments. In this paper, we propose TCP-LLM, a learning-augmented transport framework that leverages Large Language Models (LLMs) for compatibility-aware CCA selection. TCP-LLM encodes multivariate TCP time-series into token embeddings and performs single-step discrete CCA decisions through a lightweight LLM decision head. Parameter-efficient low-rank adaptation (LoRA) reduces the number of trainable parameters by approximately two orders of magnitude. Evaluation using traces collected from a physicalWi-Fi testbed with competing TCP Cubic, Bottleneck Bandwidth and Round-trip propagation time (BBR), and Performance-oriented Congestion Control (PCC) flows shows that TCP-LLM achieves the highest mean, geometric-mean, and minimum per-flow throughput among the evaluated schemes. TCP-LLM also exhibits faster and more stable training convergence and supports low-latency bounded inference. These results demonstrate the feasibility of LLM-based sequence adaptation for compatibility-aware transport-layer supervision under heterogeneous wireless conditions.	10.1109/TNSM.2026.3739364
Haipeng Yang, Yuxian Cui, Yuanyuan Ge, Yao Lu, Lei Zhang	Cross-Layer Information Fusion-Based Evolutionary Algorithm for Multi-Layer Local Community Detection	2026	Early Access	Signal detection Algorithms Merging	Local community detection in multi-layer networks has attracted considerable attention. Existing approaches typically either merge network layers before detection or detect communities independently in each layer and then combine the results. However, network-layer merging may discard layer-specific information, whereas result-level merging may hinder the extraction of information shared across layers; both limitations can bias detection. To address these issues, we propose CIF-MLCD, a cross-layer information fusion-based evolutionary algorithm for local community detection in multi-layer networks. CIF-MLCD jointly exploits common and layer-specific information. Specifically, it uses an integrated population on the merged network to capture common information and maintains a subpopulation for each layer to capture layer-specific information. We further design a network merging strategy to integrate common information across layers and a cross-layer information fusion-based knowledge transfer strategy to exchange common and layer-specific information, thereby identifying a comprehensive local community structure. This design enables the algorithm to preserve layer-specific information while exploiting cross-layer commonalities. CIF-MLCD is evaluated against five baseline algorithms on six real-world and three synthetic multi-layer network datasets. CIF-MLCD achieves the highest F1-score on four of the six real-world datasets and all three synthetic datasets, demonstrating its effectiveness across different network types.	10.1109/TNSM.2026.3751037
Ivette Kembely Carrera Manosalvas, Paulo Freitas De Araujo-Filho, Georges Kaddoum, Hadi Otrok	Unsupervised Hybrid Quantum-Classical Generative Adversarial Network-Based Intrusion Detection System for 6G-enabled Internet of Things	2026	Early Access	Modeling Signal detection Generative adversarial networks	The emergence of sixth-generation (6G) mobile networks contributes to the widespread adoption of critical Internet-of-Things (IoT) applications, such as telesurgery and autonomous vehicles. However, such advancements expand attack surfaces and make traditional security mechanisms insufficient. While machine learning (ML)-based intrusion detection systems (IDSs) have been increasingly adopted to detect attacks in such heterogeneous and dynamic environments, their complex architectures often result in high computational costs and latency, making them unsuitable for the real-time demands of 6G-enabled IoT applications. To address these challenges, we propose a novel unsupervised quantum generative adversarial network (QGAN)-based IDS. Our solution enhances the GAN generator’s ability to model complex data distributions by combining a parametrized quantum circuit with a classical neural network (NN), while maintaining a lightweight classical discriminator (fewer than 2.000 parameters) at the edge. This design significantly reduces computational complexity and minimizes detection times, since only the classical discriminator is used during inference. Experimental results demonstrate that our solution achieves competitive detection rates with an ultra-low inference latency of 0,22 ms, outperforming state-of-the-art GAN-based and transformer-based IDSs by over 2,81 times. Therefore, our architecture provides a resource-efficient security framework that delivers the ultra-low latency and high detection rates essential for next-generation IoT applications.	10.1109/TNSM.2026.3747752
Mayank Ramnani, Shasank Dixit, Sushil Yadav, Saad Ahmed, Sidharth Sharma	Split-Aware Function Placement with Availability Guarantees and Optical Provisioning in vRANs	2026	Early Access	Radio access networks 5G mobile communication Joining processes	The rapid evolution of beyond-5G and emerging 6G networks is driving the need for flexible, reliable, and cost-efficient virtualized Radio Access Network (vRAN) architectures capable of supporting heterogeneous services such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communication (mMTC). Future disaggregated RAN systems are expected to rely heavily on network slicing, functional split flexibility, and optical x-haul infrastructures to support stringent performance, scalability, and availability requirements. In this paper, we present an integrated framework for reliable, slice-aware, and functional split-aware Virtual Network Function (VNF) placement with lightpath provisioning in disaggregated vRAN environments. The proposed approach maximizes mobile network operators’ profit by jointly optimizing function placement and optical resource allocation under latency, processing, bandwidth, and availability constraints. We formulate the problem as an Integer Linear Programming (ILP) model with two variants: one that employs unshared backups and another that uses a more cost-efficient shared backup scheme. To address ILP complexity, we develop a heuristic algorithm and a GA-based metaheuristic that provide computationally efficient alternatives for larger instances. Extensive evaluations on topologies up to 128 nodes show that shared backup variants yield up to 18% higher profit, while maintaining up to 5–10% lower normalized CPU usage than unshared counterparts.	10.1109/TNSM.2026.3751852
Shuang Zheng, Xing Zhang, Michael Sheng, Haixu Wang, Wenbo Wang	Beam Hopping Low Earth Orbit Satellite Resource Allocation for Differentiated Services and Robustness Analysis under Model Attacks	2026	Early Access	Beams Satellites Resource management	Beam hopping (BH)-enabled Low Earth Orbit (LEO) satellites play a pivotal role in next-generation communication networks, providing global coverage, improving spectrum efficiency, and supporting flexible adaptation to heterogeneous service demands. To fully exploit these capabilities, artificial intelligence (AI) techniques are increasingly employed for dynamic resource allocation and power management. However, limited onboard resources and potential adversarial perturbations pose challenges to both efficiency and robustness. To address these issues, we leverage digital twin technology to accurately capture the spatio-temporal dynamics of user–satellite visibility, providing precise state information for decision-making. Building on this, we formulate a joint optimization framework for BH scheduling and power allocation as a Markov Decision Process and propose the BRIDGE—BH with Reinforcement learning incorporating Integrated Dirichlet and Gumbel-TopK Exploration—which integrates a quality of service (QoS)-driven subchannel scheduling mechanism to ensure efficient and differentiated resource allocation. The model’s robustness is systematically evaluated under three classical adversarial attacks. Simulation results demonstrate that our approach achieves superior energy efficiency, service throughput, and fairness, while the robustness analysis shows stable performance under the considered bounded adversarial perturbations.	10.1109/TNSM.2026.3710750
Huiying Jin, Jianyong Zhang, Jiajia Li, Hai Dong, Pengcheng Zhang, Jian Zhou	Dynamic Resource Allocation for Multi-Type Tasks in Mobile Edge Computing	2026	Early Access	Resource management Optimization Algorithms	We propose MD-SFLAPSO (/ˌemdi ˈsflæpsəʊ/), a Multi-type task-oriented Dynamic resource allocation method based on Shuffled Frog Leaping Algorithm and Particle Swarm Optimization for the mobile edge computing environment. MD-SFLAPSO offers an optimized resource allocation strategy. It incorporates a precise matching scheme that accounts for task requirements, collaborative node capabilities, spatial location correlation, latency constraint satisfaction, and resource supply-demand matching degree, to achieve effective collaboration. Additionally, we introduce a multi-node collaborative dynamic resource allocation strategy by leveraging user mobility patterns. Our approach is validated through a series of experiments using public data sets. The results show that MD-SFLAPSO reduces the average SLA violation rate by 8.8 percentage points from the pre-optimization level, improves average resource utilization by 9.3 percentage points over SFLA-RA, and adapts well to different resource configuration scenarios.	10.1109/TNSM.2026.3751416
Jiawei Dong, Mohsen Ghafouri, Chadi Assi	Cybersecurity Risks in Electric Vehicle Charging Management Systems	2026	Early Access	IP networks Security Internet of Things	Electric Vehicle Charging Stations (EVCSs) have become the heart of electrified transportation infrastructure. Their increasing Internet exposure makes them high-value targets for cyberattacks with potential implications for grid stability. This study presents a comprehensive, data-driven security analysis of globally exposed EV Charging Management Systems (EVCMS), bridging theoretical vulnerability concerns with quantified real-world threats. We discover and assess Internet-exposed EVCMS infrastructure, then deploy multi-component honeypot systems to capture live attack intelligence. Our novel EVCS discovery technique identifies 59,359 EVCMS IPs from 798 vendors, with notable improvements in non-English-speaking regions, overcoming traditional scanning limitations. Security assessment of 17 vulnerability types reveals most critical vulnerabilities remain unaddressed three years after coordinated disclosure. Darknet intelligence analysis reveals 10,647 unique IPs that also appeared in our EVCMS dataset participating in unsolicited scanning throughout 2024 with periodic surges. We deploy a comprehensive honeypot system with proactive engagement that actively induces compromised EVCMS devices to attack our infrastructure, spanning two monitoring phases: a ten-day period with proactive engagement and an eight-month period of long-term passive monitoring. We observed generic IoT threats and found no EVCMS-specific malware in the samples examined. A small portion of human exploration activities is found in the web portal honeypot. We observed liveness probes on both the Linux server honeypots and the OCPP backend, serving as a critical warning of imminent threats demanding immediate vendor attention before escalation to destructive campaigns.	10.1109/TNSM.2026.3751489
Qaisar Ali, Ihsan Ullah, Youn-Hee Han	Topology-Preserving Virtual Network Embedding Using Persistent Homology and Graph Attention Reinforcement Learning	2026	Early Access	Substrates Topology Joining processes	Virtual Network Embedding (VNE) maps virtual network requests (VNRs) onto shared substrate networks while satisfying node, link, and service constraints. Existing VNE methods mainly optimize resource-centric objectives such as CPU utilization, bandwidth consumption, acceptance ratio, and revenue, but often treat structural service patterns as implicit connectivity requirements. However, patterns such as redundancy loops, hierarchical aggregation, centralized control, and fault-tolerant structures may encode critical service logic, and their degradation can reduce reliability and violate service-level guarantees. This paper proposes TP-VNE, a topology-preserving and pattern-aware VNE framework that integrates persistent-homology-assisted structural characterization, graph attention-based representation learning, and proximal policy optimization. Persistent homology extracts multi-scale topological signatures from VNRs and substrate candidate regions, while complementary graph features distinguish acyclic structures such as stars and trees. A graph attention network learns topology-aware node embeddings by combining resource and structural features, and a PPO agent performs sequential node and link mapping under feasibility-aware action masking. Finally, a topology verification module validates the path-induced embedded subgraph before resource commitment. Simulation results show that TP-VNE improves the acceptance ratio from 48.5% to 83.5% at λarr = 0.20 and increases long-term average revenue from 2,780 to 4,980 compared with Baseline PPO, while achieving 89.3% pattern preservation accuracy with acceptable embedding overhead. Scalability experiments on GT-ITM substrates with up to 1000 nodes further demonstrate the robustness of TP-VNE under increasing network size.	10.1109/TNSM.2026.3744368
Gustavo Z. Bruno, Gabriel M. Almeida, Aloízio Da Silva, Luiz A. DaSilva, Joao F. Santos, Alexandre Huff, Kleber V. Cardoso, Cristiano B. Both	Towards Energy- and QoS-aware Load Balancing for 5G Advanced: Leveraging O-RAN to Achieve Sustainability and Energy Efficiency	2026	Early Access	Open RAN Modeling Timing	The increasing energy consumption of next-generation mobile networks necessitates the adoption of autonomous and energy-aware management strategies. This article proposes a novel adaptive solution leveraging the Open Radio Access Network (O-RAN) architecture to optimize energy efficiency while maintaining Quality of Service (QoS). The proposed approach introduces a hierarchical O-RAN-aligned control framework in which a Non-Real-Time RAN Intelligent Controller (Non-RT RIC) periodically computes energy-aware policies from long-term historical data, while a Near-Real-Time RIC enforces those policies through per-User Equipment (UE) handover actions on a sub-second timescale. We formulate a joint energy- and QoS-aware load balancing problem as a Mixed-Integer Non-Linear Programming (MINLP) model. This model optimizes UE association across E2 Nodes to minimize transmission power and autonomously deactivate underutilized cells, while satisfying strict QoS constraints. To validate the proposed solution, we deploy it in an experimental environment that simulates massive sports events. Experimental results demonstrate 72% energy savings over a 24-hour trace and sub-second handover delays (0.123–0.204 s/UE), confirming the solution’s feasibility for autonomous and sustainable 5G Advanced networks. This work provides guidelines for deploying energy-efficient strategies in O-RAN environments and underscores the potential of adaptive solutions for sustainable mobile communications.	10.1109/TNSM.2026.3751565
Jinzhe Wu, Quansheng Guan, Shugang Hao, Shengming Jiang, F. Richard Yu	Buffer-Induced Optimization Flow Control for Communication Networks	2026	Early Access	Joining processes Delays Throughput	Congestion is the main bottleneck for throughput improvement in communication networks. Since link buffer is an essential and early indicator for congestion, we formulate a buffer-induced optimization (BIO) flow control problem using the network utilization maximization framework. BIO uses link buffer dynamics to regulate source rates. We then derive a buffer-induced asynchronous gradient (BAG) algorithm using Lagrange duality. BAG allows links to price their buffer and allows sources to compute their rates based on path prices distributedly and iteratively. We prove the convergence and optimality of the source-link iterations under asynchronous price and rate updates. To prevent buffer bloat happening in practical networks, we also adopt a buffer shrink mechanism for BAG. Simulation results show that BAG can quickly sense the changes of network status and dynamically capture the optimal network throughput. BAG also achieves higher throughput and stronger robustness to bursts than the existing rate-based flow control and congestion control algorithms.	10.1109/TNSM.2026.3751275
Ramazan Enisoglu, Burak Alp Inan, Muhammed Ali Erbir	MSCANet: A Multi-Scale Channel Attention Network for Encrypted Traffic Classification	2026	Early Access	Modeling Transformers Convolutional neural networks	Encrypted communication has rendered traditional traffic classification ineffective, creating challenges for QoS provisioning, network management, and security monitoring. Existing deep learning approaches exploit either spatial or sequence-level features in isolation, without jointly modeling multi-scale byte patterns and inter-packet ordering. We propose MSCANet, a hybrid architecture integrating a multi-scale 1D convolutional module, a sequentially applied Efficient Channel Attention (ECA) module, and a Transformer encoder, combining the two feature streams through a learnable weighted concatenation. With approximately 1.18M parameters, MSCANet attains 95.14% mean accuracy on the 20-class USTC-TFC2016 dataset and 92.61–98.01% across three 10-class sub-tasks over five random seeds, at a median per-sample inference latency of 1.29 ms on an NVIDIA RTX 5090 Laptop GPU. Against five baselines re-trained under an identical pipeline and schedule, spanning classical machine learning, CNN-only, attention-BiLSTM, and hybrid CNN–Transformer families, MSCANet is the most accurate on all four sub-tasks, improving over the strongest classical baseline by 6.16 pp and over the closest neural baseline by 2.38 pp while using 17.5% of its parameters and running 3.1× faster. These gains are demonstrated against the five re-trained baselines. Pretrained Transformers such as ET-BERT and YaTC report higher accuracy in their original publications, under their own preprocessing and with a pretraining stage that MSCANet does not use; the advantages we expect relative to them (no pretraining stage and, against ET-BERT, a model two orders of magnitude smaller) were not measured under our protocol, and we position MSCANet within the train-from-scratch regime.	10.1109/TNSM.2026.3748386
Jian Liu, Jinhong Chen, Eldar Sabanovic, Jinliang Liu, Engang Tian, Oh-Min Kwon	Predictor-Enhanced Constrained Reinforcement Learning for Networked CAV Platoons: A Dual-Redundancy Scheme	2026	Early Access	Timing Vehicles Learning (artificial intelligence)	This paper investigates resilient tracking control for connected and automated vehicle (CAV) platoons under false data injection (FDI) attacks and prescribed spacing constraints. A kinematics-based state predictor is combined with a dynamic event-triggered mechanism (ETM) to provide smooth trajectories from sparse, FDI-corrupted measurements while reducing communication use. Prescribed performance control (PPC) maps the predicted spacing error into an unconstrained state for continuous-time actor-critic reinforcement learning (RL). The actor-critic controller learns an approximate tracking policy without identifying the unknown nonlinear drift. A locally sensed, saturated proportional-derivative fallback controller can override the learned command when the measured spacing error approaches the prescribed boundary. These two control paths form the dual-redundancy scheme. The analysis excludes finite-time accumulation of triggering instants, establishes input-to-state stability of the prediction-error subsystem, and gives conditional ultimate bounds for the prediction error and sampled adaptive weights under the stated input and approximation-domain conditions. Matched ablations and multi-seed tests evaluate communication use, fallback activation, and the responses to scalar and vector attacks.	10.1109/TNSM.2026.3748969
Jian Ye, Gaolei Fei, Jiajie Li, Xuemeng Zhai, Guangmin Hu, Ming Liang	Network topology inference via time-frequency consistency analysis of end-to-end traffic time series	2026	Early Access	Timing Topology Measurement	In this paper, we study the classical network tomography problem of tree topology inference using end-to-end measurement. Traditional methods rely on time series correlations to measure shared path lengths. However, time-varying and non-stationary background traffic often biases these measurements. To overcome this, we comprehensively explore end-to-end traffic time series via time-frequency consistency analysis. Specifically, we jointly characterize time series in both time and frequency domains, imposing a consistency constraint. This ensures embeddings of the same time series across domains remain close in the latent space, providing a robust description of traffic dynamics. Our contrastive learning approach effectively highlights similarities among flows on common paths and distinguishes those on disjoint paths, thereby mitigating background traffic interference. Extensive evaluations across widely used probing strategies and metrics demonstrate that our embedding significantly enhances time series correlation and accurately measures shared paths.	10.1109/TNSM.2026.3749558
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
Shubhani Aggarwal, Georges Kaddoum	QSB-SHA: Quantum-secured Blockchain for Smart Home Authentication	2026	Early Access	Authentication Smart homes Internet of Things	Today, IoT enables smart home users to remotely connect with household appliances via the Internet. Sensor-equipped smart devices are widely deployed in smart homes to collect and manage information regardless of user location. However, ensuring the secure and private accessibility of these devices in a centralized manner remains a major concern. The emergence of blockchain and quantum computing, with their enhanced information processing capabilities on decentralized platforms, offers a viable solution to this issue. Consequently, there is a need for cryptographic primitives that can withstand both quantum and classical attacks. To address this concern in the present study, we propose a quantum-secured blockchain (QSB) framework for smart home authentication (SHA). The proposed framework employs quantum key distribution (QKD) and maximally entangled states on a decentralized platform to enable secure key distribution and mutual authentication between smart home users and devices. Furthermore, our approach integrates quantum passwords and quantum digital signatures to verify and validate legitimate users, thereby ensuring security and privacy. The framework is simulated using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool for formal security analysis. We also conduct an informal security analysis and compare the performance of our proposed scheme with that of the previously proposed solutions. The results demonstrate that our framework outperforms existing approaches across various performance evaluation metrics.	10.1109/TNSM.2026.3747090