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
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
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
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
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
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
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
Kim Hammar, Neil Dhir, Rolf Stadler	Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Search	2026	Early Access	Modeling Timing Trees (botanical)	The CAGE-2 challenge is considered a standard benchmark to compare methods for autonomous cyber defense. Current state-of-the-art methods evaluated against this benchmark are based on model-free (offline) deep reinforcement learning techniques, which do not provide provably optimal defender strategies. We address this limitation and present a formal (causal) model of CAGE-2 together with a method that converges to a provably optimal defender strategy, which we call causal partially observable Monte-Carlo planning (C-POMCP). Our method has two novel properties. First, it incorporates the causal structure of the target system through causal relationships among the system variables. This structure allows for a significant reduction of the search space of defender strategies. Second, it is an online method that updates the defender strategy at each time step via tree search. Evaluations against the CAGE-2 benchmark show that C-POMCP achieves state-of-the-art performance with respect to effectiveness and requires two orders of magnitude less computation than the closest competitor method.	10.1109/TNSM.2026.3735865
Jianer Zhou, Xinyi Qiu, Zhenyu Li, Gareth Tyson, Encheng Yu, Weichao Li, Heng Pan, Xinyi Zhang, Zhiwei Xu	Themis: An Adjustable Congestion Control Framework for Improving Video QoE	2026	Early Access	Quality of experience Videos Fluid flow	Optimizing congestion control algorithms (CCAs) has the potential to enhance video quality of experience (QoE). The goal of this work is to devise a congestion control framework that (i) ensures that individual users enjoy high video QoE, while (ii) minimizing variance, such that QoE is fairly distributed across all users, especially in fluctuating network, such as cellular network. We present Themis, a video-centric congestion control framework. Themis first uses a distributed approach to allocate a fair target QoE for each client. Based on this fair QoE, Themis then selects congestion control actions to optimize for video QoE (rather than throughput) based on application-layer signals provided by the client. Thus, rather than trying to maximize a flow’s (fair) share of bandwidth, Themis optimizes a flow’s share of the QoE budget. We evaluate Themis in both emulated and production networks. We show that in cellular network Themis achieves a 12.4% QoE improvement compared with BBR, and 37.1% QoE standard deviation decrease compared with the state-of-the-art, Minerva.	10.1109/TNSM.2026.3732350
Junior Momo Ziazet, Brigitte Jaumard	Energy Efficient Placement of Logical Functionalities in 5G Networks	2026	Early Access	Energy Copper Modeling	Although 5G networks are more efficient in terms of power consumption to traffic ratio, efforts still need to be made to further increase energy efficiency not only for the radio part, but also with respect to the growing cloud component with edge servers. Consolidation of traffic workloads onto shared infrastructures is a key feature of cloud computing to reduce energy consumption, and logical functionality placement plays a key role in this regard. Here, in the cloud RAN context, we propose a unified and energy-aware logical placement of 5G E2E functionalities, i.e., distributed units (DUs), centralized units (CUs), and user plane functions (UPFs), together with traffic routing. The placement problem is formulated as a large-scale integer linear program and solved using a column generation-based decomposition technique, complemented by an efficient heuristic to ensure tractability and improved scalability. The model captures key network and cloud (compute) resources, jointly optimizing the placement of DU, CU, and UPF components, along with traffic routing, to minimize energy consumption while maintaining low latency and high Quality of Service (QoS). Numerical results, based on an open Montreal traffic dataset, demonstrate that the proposed column generation algorithm achieves near-optimal solutions, while the heuristic approach offers significantly better scalability with consistently strong performance. The proposed methods reduce energy consumption by up to 14% and maintain low-latency service delivery. Furthermore, the results highlight that static, peak-time-based placement strategies can lead to inefficiencies throughout the day, emphasizing the importance of accounting for broader temporal traffic patterns.	10.1109/TNSM.2026.3729149
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
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
Sheng-Shan Chen, Ren-Hung Hwang, Ying-Dar Lin, Tun-Wen Pai, Chin-Yu Sun	Extracting Attack Pattern from WAF Logs and CTIs Using Contrastive Semantic Learning	2026	Early Access	Modeling Payloads Cyber threat intelligence	Web Application Firewalls (WAFs) are widely deployed to protect web services, but their rule-based design provides limited visibility into attacker intent. WAF logs consist primarily of low-level HTTP artifacts that lack the behavioral context required for effective threat analysis. To address this limitation, we propose the first automated framework that mapsWAF logs to MITRE ATT&CK techniques through CTI-grounded semantic learning. The approach integrates structure-aware Monte Carlo Tree Search-based payload generation, CodeBERT-driven contrastive learning for attack classification, and cyber threat intelligence (CTI) alignment for TTP retrieval. The framework is evaluated on over 714,000 WAF logs derived from validated attack payloads across eight attack types, generated within a controlled environment using ModSecurity and OWASP Core Rule Set (CRS). Experimental results demonstrate 99.38% multi-class classification F1 score and identification of 206 unique ATT&CK techniques. Compared with a Rule-ID Heuristic baseline derived from OWASP CRS rule semantics, the proposed framework identifies 7.4× more unique ATT&CK techniques and provides substantially broader TTP-level visibility. External validation on a real-world ModSecurity log dataset further demonstrates that the framework preserves reliable classification and retrieval performance beyond the controlled payload-generation setting.	10.1109/TNSM.2026.3738730
Yingjie Hu, Weiping Wang, Shigeng Zhang, Hong Song, Ziheng Huang, Song Guo	Dual-State Representation Learning for Multi-Granularity IoT Device Identification	2026	Early Access	Internet of Things Modeling Training	The rapid growth of IoT devices has increased demand for traffic-based network asset management and security monitoring. Most existing methods operate in closed-set settings and may misclassify unseen devices as known models or return only an unknown label. To address this problem, this paper proposes a multi-granularity IoT device identification method based on dual-state representation learning. Device identity is modeled at three levels: type, manufacturer, and model, retaining type and manufacturer information when the device model cannot be reliably identified. The method extracts statistical, sequence, and raw-byte features and learns sequence and byte embeddings from idle and behavior traffic. Self-supervised learning and contrastive learning are used to improve the discriminative ability of representations. A state-aware gating mechanism then dynamically fuses the dual-state embeddings. Multi-task classification heads and confidence thresholds are used to support joint identification and rejection. Experiments on three public datasets show over 98% accuracy for known-device identification. The method also achieves over 97% accuracy for type and manufacturer prediction on the unknown-model test set and over 95% rejection rate for unknown models. Online deployment achieves an average latency of 3.1 ms and a throughput of 322 samples/s, demonstrating practical potential in open network environments.	10.1109/TNSM.2026.3738728
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
Xiaodi Wang, Yunwei Dong, Weizhi Meng, Meng Li, Yining Liu	Dropout-Tolerant Privacy-Preserving Aggregation for Federated Mobile Crowdsensing	2026	Early Access	Modeling Privacy Internet of Things	Federated Learning (FL) has emerged as a key enabler for privacy-preserving, decentralized sensing systems, giving rise to Federated Mobile Crowdsensing (F-MCS). A well-known bottleneck in such systems is the inefficiency of synchronous training, which stalls for all participants and is susceptible to stragglers in heterogeneous environments. Although asynchronous FL methods have been explored to alleviate this, they often introduce the critical issue of stale updates, which can degrade model convergence and accuracy. To simultaneously address the challenges of efficiency, staleness, and robustness, this paper proposes a novel Dropout-Tolerant Privacy Aggregation (DTPA) scheme for FL that operates without a trusted third party (TTP). Our solution leverages the distributed decryption feature of the lifted EC-ElGamal cryptosystem to enable secure, decentralized model aggregation. We further introduce an efficient worker selection algorithm to systematically reduce waiting time. Moreover, a dedicated dropout-tolerant mechanism is developed to maintain protocol execution even under a high rate of client failures, thereby enhancing robustness. Security analysis confirms that our scheme fulfills essential privacy and security requirements. Extensive simulations demonstrate that the proposed DTPA scheme significantly improves training efficiency and convergence stability compared to state-of-the-art methods, while remaining practical for deployment on resource-constrained mobile devices.	10.1109/TNSM.2026.3732465
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
Mubashir Murshed, Glaucio H. S. Carvalho, Robson E. De Grande	Holistic Intelligent Traffic Steering Management in Multi-RAT Vehicular Networks	2026	Early Access	Radio access technologies Rats Vehicles	Multiple Radio Access Technology (multi-RAT) environments provide a promising foundation for service-aware communication in intelligent transportation systems (ITS) and smart cities. However, traffic steering (TS) in highly mobile and ultra-dense vehicular networks remains challenging due to dynamic network conditions, heterogeneous RAT capabilities, varying vehicle requirements, packet loss, latency, and frequent ping-pong RAT switching. In this context, we propose Holistic Intelligent Traffic Steering (HITS), a proactive bi-level TS management framework for multi-RAT vehicular networks. HITS integrates centralized network-wide guidance with local vehicleside decision-making. At the central level, a Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model captures holistic spatio-temporal network dynamics and evaluates RAT optimality. At the local level, a State-Action-Reward- State-Action (SARSA) reinforcement learning agent performs adaptive, vehicle-specific RAT selection using local observations and central-level optimality guidance. Results show that HITS achieves up to 6.5% higher average throughput, reduces packet loss ratio by more than 30.2%, lowers latency by nearly 12.2%, and reduces the ping-pong RAT switching rate by over 24% compared with baseline and state-of-the-art (SoTA) TS approaches.	10.1109/TNSM.2026.3729840
Deemah H. Tashman, Soumaya Cherkaoui	Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks	2026	Early Access	Reconfigurable intelligent surfaces Reliability Optimization	Cognitive radio networks (CRNs) are a key mechanism for alleviating spectrum scarcity by enabling secondary users (SUs) to opportunistically access licensed frequency bands without harmful interference to primary users (PUs). To address unreliable direct SU links and energy constraints common in next-generation wireless networks, this work introduces an adaptive, energy-aware hybrid reconfigurable intelligent surface (RIS) for underlay multiple-input single-output (MISO) CRNs. Distinct from prior approaches relying on static RIS architectures, our proposed RIS dynamically alternates between passive and active operation modes in real time according to harvested energy availability. We also model our scenario under practical hardware impairments and cascaded fading channels. We formulate and solve a joint transmit beamforming and RIS phase optimization problem via the soft actor-critic (SAC) deep reinforcement learning (DRL) method, leveraging its robustness in continuous and highly dynamic environments. Notably, we conduct the first systematic study of reward poisoning attacks on DRL agents in RIS-enhanced CRNs, and propose a lightweight, real-time defense based on reward clipping and statistical anomaly filtering. Numerical results demonstrate that the SAC-based approach consistently outperforms established DRL base-lines, and that the dynamic hybrid RIS strikes a superior trade-off between throughput and energy consumption compared to fully passive and fully active alternatives. We further show the effectiveness of our defense in maintaining SU performance even under adversarial conditions. Our results advance the practical and secure deployment of RIS-assisted CRNs, and highlight crucial design insights for energy-constrained wireless systems.	10.1109/TNSM.2026.3660728
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