TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Mohamed Zalat, Chris Barber, Babak Esfandiari, Thomas Kunz	A Reusable Network Digital Twin Architecture for QoS-Centric Network Management	2026	Early Access	Modeling Fluid flow Optimization	We propose a network digital twin approach for Quality of Service (QoS)-centric network management and demonstrate it on multiple network management problems. Our network digital twin involves running many ”what-if?” network configurations using a fast inference model for predicting network behavior, and applying the best configuration found based on the criteria of the network operator. We demonstrate the flexibility of this approach by applying it to 3 different network management problems: Interior Gateway Protocol (IGP) weight optimization, Border Gateway Protocol (BGP) route assignments, and gray fault detection and localization. We test our approach for each application on various OMNeT++ topologies and compare it to existing benchmarks in the respective literature. Our results indicate that the proposed network digital twin approach performs comparably to existing benchmarks in the network management problems explored and sometimes outperforms them in quality of service metrics.	10.1109/TNSM.2026.3737654
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
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
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
Yao Xin, Yuqiao Luo, Shufan Cao, Chongwu Dong, Qingfeng Tan	HBT: A Hybrid Bidding Tree for High-Performance Packet Classification	2026	Early Access	Heterojunction bipolar transistors Trees (botanical) Vegetation	Traditional packet classification algorithms based on decision trees often rely on rule replication to increase lookup speed, which inevitably leads to memory explosion. Conversely, existing zero-replication methods frequently suffer from extreme tree depth and structural fragmentation. To address this dilemma, this paper proposes the Hybrid Bidding Tree (HBT), a high-performance architecture designed to enforce zero-replication while sustaining deterministic lookup throughput. First, HBT employs an Overlap-Aware Rule Decomposition (OARD) framework to proactively isolate topologically entangled rules, purifying the primary geometric space. Second, HBT introduces a dynamic competitive bidding mechanism for tree construction. At each node, a discrete bit-selection path and a continuous range-partitioning path compete to determine the optimal splitting strategy based on local geometric heterogeneity. Finally, to guarantee an O(N) memory boundary, unpartitionable residual rules are assigned to a single-level Onion-Peeling fallback structure, preserving linear memory growth while introducing additional sequential checks in the auxiliary path. Experimental evaluations on ClassBench-ng rulesets containing up to 256k rules demonstrate the efficacy of HBT. Compared with state-of-the-art algorithms such as PT-Tree and TupleTree, HBT achieves the highest lookup throughput across all twelve evaluated rulesets at both the 128k and 256k scales, while maintaining strong memory efficiency and highly competitive construction and update latencies.	10.1109/TNSM.2026.3734240
Hussein Fawaz, Jacopo Talpini, Marco Savi, Silvia Giordano, Omran Ayoub	Detecting Zero-Day Attacks via Reconstruction of Feature Influence and Model Uncertainty	2026	Early Access	Modeling Uncertainty Training	In practical Network Intrusion Detection System (NIDS) deployments, detecting anomalies is only the first step, while determining the exact nature of those anomalies is equally important. Commonly, anomalous traffic is forwarded to a supervised multiclass classifier trained to identify known attack categories. While effective for known threats, this step presents a significant limitation, as zero-day attacks can be misclassified as known attacks. Therefore, there is a need for approaches that go beyond standard classification and can reliably recognize when an input does not conform to any learned attack pattern, i.e., zero-day attacks. To tackle this problem, we propose a novel detection strategy that leverages per-instance feature importance scores from an explainable Artificial Intelligence (XAI) framework and prediction uncertainty estimates derived from an ensemble classifier. To evaluate our approach, we conduct extensive experiments using a leave-one-attack-out strategy across three benchmark datasets, CICIoT2023, NF–TON–IoT, and CIC–DDoS2019, and test performance under two underlying classifiers, namely XG-Boost and Random Forest, demonstrating the model-agnostic nature of our method. Experimental results show that our approach achieves best-case AUROC gains approaching 40% and F1-score improvements of up to 73%, while maintaining positive or near-neutral worst-case performance across datasets, highlighting the effectiveness and robustness of jointly modeling explanation-driven reconstruction error and predictive uncertainty for reliable zero-day threat identification.	10.1109/TNSM.2026.3731401
Kim Hammar, Rolf Stadler	Online Identification of IT Systems through Active Causal Learning	2026	Early Access	Modeling Learning (artificial intelligence) Costing	Identifying a causal model of an IT system is fundamental to many branches of systems engineering and operation. Such a model can be used to predict the effects of control actions, optimize operations, diagnose failures, detect intrusions, etc., which is central to achieving the longstanding goal of automating network and system management tasks. Traditionally, causal models have been designed and maintained by domain experts. This, however, proves increasingly challenging with the growing complexity and dynamism of modern IT systems. In this paper, we present the first principled method for online, data-driven identification of an IT system in the form of a causal model. The method, which we call active causal learning, estimates causal functions that capture the dependencies among system variables in an iterative fashion using Gaussian process regression based on system measurements, which are collected through a rollout-based intervention policy. We prove that this method is optimal in the Bayesian sense and that it produces effective interventions. Experimental validation on two testbeds shows that our method enables accurate identification of a causal system model while inducing low interference with system operations.	10.1109/TNSM.2026.3736090
Messaoud Ait-Yahia, Wael Jaafar, Rami Langar	Joint Design of Blockchain-Enabled Service Placement and Task Assignment in Vehicular Fog Computing Networks	2026	Early Access	Delays Timing Optimization	Driven by the evolution of blockchain and fog computing, vehicular networks are increasingly capable of supporting latency-sensitive applications with enhanced security and trust guarantees. However, the joint resource allocation for task offloading and blockchain services has been insufficiently investigated in existing works. To address this gap, this paper proposes a framework for jointly allocating resources of blockchain, users’ virtualized services, and Mobile Edge Computing (MEC) task assignment in Vehicular Fog Computing (VFC) networks. Specifically, we formulate the optimization problem as an integer nonlinear programming model aiming to maximize the satisfaction rate of users’ service requests while minimizing the corresponding blockchain operation time under mobility, queuing, instantiation, and resource constraints. To solve it in a timely manner, we design two-stage hierarchical low-complexity solutions, namely a Particle Swarm Optimization-based Joint Blockchain-enabled Service placement and Task Assignment algorithm (PSO-JBSTA), and a Genetic Algorithm-based approach (GA-JBSTA). Through extensive simulations, we demonstrate the effectiveness of PSO-JBSTA (resp. GA-JBSTA) and their adaptability to network conditions, achieving an average 35% (resp. 24%) improvement in users’ service satisfaction rate and 9.5% (resp. 10.2%) reduction in average blockchain validation delay compared with the baselines.	10.1109/TNSM.2026.3737068
Heng He, Qin Xu, Hai Yu, Lei Nie, Jianfeng Lu	LFNC: A Lightweight and Fine-Grained Two-Stage Network Flow Classification Framework with Programmable Data Planes	2026	Early Access	Fluid flow Planing Modeling	Flow classification is a crucial component of network intrusion detection systems. Existing approaches mainly fall into two categories: in-network classification and control-data plane collaborative classification. The former is constrained by the computing and memory resources of programmable switches, often sacrificing classification accuracy and efficiency. The latter requires transmitting large volumes of packets to the control plane, leading to high processing latency, excessive control-channel overhead, and limited flow coverage. To address these challenges, we propose LFNC, a Lightweight and Fine-grained two-stage Network flow Classification framework with programmable data planes. In the first stage, LFNC introduces a Decision Tree Segmentation (DTS) algorithm to train resource-aware models in the control plane. The trained DTS models are converted into switch-compatible matching rules and deployed in the data plane to perform line-rate binary classification for preliminary anomaly detection. In the second stage, LFNC employs a cuckoo filter together with dual circular queues to selectively buffer essential packet features of preliminarily anomalous flows in the data plane and efficiently transfer them to the control plane. A multi-class energy-based flow classifier is then applied in the control plane to achieve accurate and fine-grained classification of anomalous flows. Experimental results on the Tofino hardware switch demonstrate that LFNC outperforms eight state-of-the-art baselines, improving flow collection rate by 1.07% and classification accuracy by 3.34%, while significantly reducing hardware resource consumption and maintaining low packet processing latency.	10.1109/TNSM.2026.3738578
Awais Bilal, Kashif Sharif, Liehuang Zhu, Fan Li, Chang Xu	SLA-Aware RSU-Edge Delegate Orchestration for IoV Consensus	2026	Early Access	Modeling Information rates Throughput	Ensuring reliable and timely consensus among Internet of Vehicles (IoV) nodes is critical for safety and operational efficiency, particularly under high mobility and dynamic network conditions. Traditional consensus protocols, however, do not explicitly incorporate service-level objectives (SLOs) such as commit latency, tail latency, or delegate-set diversity, limiting their applicability in real-world deployments. In this paper, we present a service-level agreement (SLA)-aware road-side unit (RSU)-edge orchestration framework for IoV consensus delegate selection, which leverages reinforcement learning (RL) to optimize committee composition while preserving quorum safety. Our approach embeds SLO metrics directly into the proximal policy optimization (PPO) reward function, enabling the RSU-edge to adapt delegate selection online under varying vehicle densities, speeds, and network conditions. A shortlist-based candidate reduction mechanism reduces computational overhead, while certificate-governed reconfiguration and state transfer support safe committee activation and recovery. Extensive simulations across multiple scenarios, including burst losses and mobility-induced churn, demonstrate that our method reduces median and tail commit latency, increases throughput, and maintains higher delegate-set diversity than baseline heuristics and the adapted BFTBrain-style service comparator. Under the simulator reference configuration, the modeled proposal-construction components yield a component-wise tail budget of 18.8 ms, excluding governance certification and state synchronization. The framework provides a practical blueprint for service-level-aware management of IoV consensus, bridging the gap between protocol-level designs and operational network management. Within the controlled service-level simulation scope, the study demonstrates the feasibility, robustness, and performance advantages of RL-driven RSU-edge orchestration. Packet-level and field deployment validation remain future work.	10.1109/TNSM.2026.3739347
Amr Aboeleneen, Mohamed Abdallah, Aiman Erbad, Amr Salem	CIVIC: Cooperative Immersion Via Intelligent Credit-sharing in DRL-Powered Metaverse	2026	Early Access	Resource management Modeling Metaverse	The Metaverse faces complex resource allocation challenges due to diverse Virtual Environments (VEs), Digital Twins (DTs), dynamic user demands, and strict immersion needs. This paper introduces CIVIC (Cooperative Immersion Via Intelligent Credit-sharing), a novel framework optimizing service-profile provisioning and budget-credit sharing among multiple Metaverse Service Providers (MSPs) to enhance user immersion. Unlike existing methods, CIVIC integrates VE rendering, DT synchronization, credit sharing, and immersion-aware provisioning within a cooperative multi-MSP model. The resource allocation problem is formulated as two NP-hard challenges: a non-cooperative setting where MSPs operate independently and a cooperative setting utilizing a General Credit Pool (GCP) for dynamic budget support. Using Deep Reinforcement Learning (DRL) for tuning resources and managing cooperating MSPs, CIVIC achieves 12-36% higher request completion, 23-70% higher fulfillment rates, 20-60% more served clients, and up to 51% more fairly distributed requests, all with competitive costs. Extensive experiments demonstrate CIVIC’s resilience, adaptability, and robust performance under dynamic load conditions and unexpected demand surges, making it suitable for real-world distributed Metaverse infrastructures.	10.1109/TNSM.2026.3737119
João Gabriel Pazinato De Bittencourt, Fábio Gonçalves De Oliveira, Edson José Pacheco, Carlos Marcelo Pedroso	Packet-Level Causal Certification of Tail-Latency and Reliability Control in O-RAN Slicing	2026	Early Access	Radio access networks Regional area networks Context	An O-RAN slice controller holds two levers, the resource-block budget and the scheduling discipline, and reads an aggregate proxy that can pass a slice dropping packets or overrunning its latency bound. A lever that correlates with a key performance indicator need not control it. We answer, at the packet level, which lever an operator may act on. We evaluate compliance over every generated packet and lower-bound the probability that a lever both achieved compliance and was necessary for it. The bound carries information only when computed within each operating context and then standardized. The other order reduces to the average treatment effect, and the two coincide whenever the lever effect keeps its sign. A zero-sum allocation lever breaks that condition: the budget it grants one slice is taken from another. Over 3,720 runs one lever effect reverses from +0.32 to −0.66 with the neighbour’s load while averaging −0.008, so the pooled bound is zero and the stratified bound 0.156. Two contrasts on that lever, averaging −0.008 and 0.000, certify at 0.156 and at zero, which the pooled average effect cannot separate. The error guarantee covers false declarations of an effect, not the safety of acting on one.	10.1109/TNSM.2026.3740306
Le Zhang, Yu Gu, Ye Du, Xin Liu, Jikai Zhang, Junyan Guo	EasySatSim: Enabling Researchers to Build Scalable LEO Satellite Network Experimental Environments on Personal Computing Devices	2026	Early Access	Satellites Protocols Low earth orbit satellites	Global communication networks based on LEO satellite constellations are within reach, attracting the attention and efforts of many researchers. However, creating the required experimental environments under the complex and vast satellite network architecture remains a challenge. As of now, open-source experimental platforms generally face limited adaptability, high resource consumption, and difficulties in environment deployment. Therefore, this paper introduces the EasySatSim experimental platform, which allows researchers to build large-scale LEO satellite network experimental environments on personal computing devices. EasySatSim consists of three core components: entities, behaviors, and protocol stacks, and constructs a highly modular architecture through the Controller Layer, Manager Layer, Execution Layer, Global Services Layer, and API Support Layer. Researchers can configure specific tasks for individual satellites and users, and even create entities like ground stations and central servers as needed, supporting adaptability from the parameter level to the scenario level. EasySatSim also considers packet-level system overhead and provides configurable support for practical network-level performance evaluation. Finally, three cases from the distinct fields of intrusion detection, machine learning, and network routing in LEO satellite networks are used to demonstrate the flexibility of EasySatSim.	10.1109/TNSM.2026.3739052
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
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 Internet Measurement	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
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
Marco Garofalo, Luca D’Agati, Laura García, Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez, Joan Garcia-Haro, Antonio Puliafito, Giovanni Merlino, Francesco Longo	Trustless SLA Enforcement and Roaming in LoRaWAN through Smart Contracts	2026	Early Access	Roaming Service level agreements LoRaWAN	LoRaWAN is widely used for Internet of Things (IoT) services that require long-range, low-power wireless connectivity. As deployments grow, roaming between different network operators becomes increasingly important to maintain service continuity for mobile IoT devices. In practice, however, roaming still depends on bilateral agreements and trusted intermediaries, which limit scalability and reduce transparency in multi-operator settings. This work introduces a blockchain-based roaming architecture that uses Algorand smart contracts to automate Service Level Agreement (SLA) management between providers. The system supports dynamic roaming agreements, immutable packet accounting, and transparent settlement. In our system, the enforced guarantee concerns forwarding-level service quality at the roaming interface, namely payment conditional on observed delivery ratio, rather than deterministic radio-layer latency or jitter guarantees. We implemented the full infrastructure, including a custom Gateway Bridge that extracts the Network Identifier (NetID), a blockchain service that interacts with Algorand smart contracts for SLA validation, and a decentralized provider catalog for operator discovery. We evaluated the system on a testbed with production-grade ChirpStack network servers and compared it with our previous non-blockchain implementation. Both versions achieve comparable throughput (5800–5900 packets/minute with 1000 devices) and maintain 99% packet forwarding efficiency. Blockchain integration adds measurable overhead, including a forwarding latency overhead in the 400–490 ms range for SLA validation, largely independent of the underlying network delay, but remains acceptable for delay-tolerant IoT services. Overall, the results show that decentralized LoRaWAN roaming can be implemented without breaking compatibility with existing network architectures.	10.1109/TNSM.2026.3734694
Abdul Samim, Attiq Ur Rehman, KyungHi Chang	Intelligent Handover Management for 6G LEO Satellite Constellations: A Predictive Multi-Agent PPO Approach	2026	Early Access	Satellites Handover Loading	The integration of Low Earth Orbit (LEO) satellite constellations into 6G networks promises ubiquitous connectivity, yet poses unprecedented challenges for handover management due to rapid orbital motion and dynamic channel conditions. Traditional reactive handover algorithms, designed for quasistatic terrestrial networks, fail to address the multi-dimensional optimization requirements of LEO systems where satellites move at velocities exceeding 7 km/s and user-satellite connections last only 2-4 minutes. This paper proposes a Predictive Multi-Agent Proximal Policy Optimization (PMA-PPO) framework for SNR-aware load-balanced handover management in dual-layer LEO satellite networks. The framework integrates three core components: Gated Recurrent Unit (GRU) networks for temporal forecasting of channel conditions and satellite loads, distributed PPO agents for autonomous handover decision-making, and a coordination mechanism that balances signal quality with load distribution. Through comprehensive simulations of a realistic dual-layer constellation, PMA-PPO achieves significant performance improvements: up to 77.7% reduction in handover failure rates, 74.06% reduction in satellite overload duration, 35.13% improvement in throughput fairness, and ping-pong handover rates consistently below the practical 5% threshold across all load conditions, compared to state-of-the-art base-line approaches. The proposed approach achieves polynomial computational complexity versus exponential cost for exhaustive optimization, making it suitable for real-time deployment in large-scale LEO constellations.	10.1109/TNSM.2026.3735527
Nilesh Chakraborty, Petar Djukic, Burak Kantarci	Aggressive-YoYo: Exploiting Intent-Semantic Misalignment in AI-Native 6G Management Planes	2026	Early Access	Central Processing Unit Management Modeling	Intent-Based Networking (IBN) enables operators to express high-level service objectives that are automatically translated into low-level control and orchestration policies. In AI-native 6G management planes, semantic misalignment during this translation can induce unsafe configurations that amplify conventional resource-exhaustion attacks. We investigate this vulnerability through aggressive-YoYo, a compound threat combining YoYo-style burst traffic with prematurely configured Kubernetes readiness probes. We implement an end-to-end Intent-to-Configuration pipeline that resolves natural-language service intents into structured policies, compiles them into Kubernetes probe settings, and evaluates the resulting behavior using a representative slice-assurance management function on Google Kubernetes Engine (GKE). Controlled readiness-delay experiments show that premature readiness can increase replica provisioning, aggregate CPU and memory consumption, storage activity, and request failures, while inducing non-trivial service-level tradeoffs. Similar resource amplification under a different N1-family machine type and deployment zone indicates that the effect is not specific to a single configuration.We further analyze readiness misconfiguration across multiple Kubernetes scaling mechanisms and derive service-specific safe and amplifying configuration regions. From the detection perspective, we show-case that aggressive-YoYo is detectable using fully supervised temporal classifiers evaluated with cycle-disjoint testing and feature-set ablation; the best configuration achieves an average accuracy of 92.6%. Under scarce aggressive-YoYo supervision, i.e., limited exposure to aggressive-YoYo traces, the supervised approach improves detection over the one-class setting. These results show that intent-semantic misalignment creates measurable cross-layer management risks and motivate semantic validation and telemetry-aware monitoring for trustworthy AI-native 6G orchestration.	10.1109/TNSM.2026.3736983
Amr Aboeleneen, Mohamed Abdallah, Aiman Erbad, Amr Mohamed	ZTCI: Zero-Touch Cooperative Immersion, a Plug-and-Deploy Deep Reinforcement Learning-Based Framework for Resource Allocation and Cooperation in the Metaverse	2026	Early Access	Resource management Modeling Metaverse	The Metaverse requires edge providers, termed Metaverse Service Stations (MSSs), to provision high-quality virtual environments (VEs) and faithful digital twins (DTs) for heterogeneous virtual venues under tight compute and network budgets. In multi-MSS deployments, localized demand surges can overwhelm one station while neighboring stations remain underutilized. Cooperation is therefore essential but challenging because venue requirements are heterogeneous, demand is bursty, and proximity-based neighborhood sets change over time. These dynamics motivate zero-touch operation that requires neither retraining nor reconfiguration at deployment. We introduce the General Optimized Agent (GOA), a plug-and- deploy Deep Reinforcement Learning (DRL) agent that jointly optimizes per-venue VE/DT service levels and inter- MSS resource sharing. GOA is trained through staged curriculum learning and uses a position-aware set encoder with learned positional embeddings and attention-based pooling to map variable-size neighbor sets to fixed-dimensional representations. This design yields a single policy that generalizes across MSS types, budget levels, and dynamic team sizes. Extensive evaluation shows that GOA improves request satisfaction, load balancing, and cost efficiency over representative baselines, supporting scalable, zero-touch Metaverse cooperation under realistic infrastructure constraints.	10.1109/TNSM.2026.3737187