TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
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
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
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
Ahlem Menaceur, Hamza Drid, Mohamed Rahouti, Yasmine Medjadba, Abdellah Chehri	Priority-Aware Dual-Queue Buffering for Reactive SDN Fault Management with Reduced Loss	2026	Early Access	Software defined networking Joining processes Loading	Software-Defined Networking (SDN) provides controller-driven flexibility for network and service management, yet reactive failure recovery often suffers from a recovery gap between fault detection and the installation of updated forwarding rules. Packets arriving during this interval are vulnerable to loss, while rerouted flows may overload specific links, undermining service continuity. To address these challenges, this paper proposes a dual-priority, queue-assisted reactive recovery mechanism that integrates traffic classification and buffering at the data plane. The design assigns flows to a high-priority queue (HPQ) or a low-priority queue (LPQ), ensuring that critical traffic is preserved while the controller completes rerouting. Evaluation on a 9-switch custom topology and the 16-node NSFNET backbone demonstrates that the proposed approach reduces packet loss (from 10.3% to 5.4% in the 9-switch topology and to below 1.9% in NSFNET) and improves post-failure load distribution, decreasing congested and surcharged links by 37.6% and 20.6%, respectively, compared to conventional reactive recovery. These results highlight that priority-aware buffering during the recovery gap enhances resilience, scalability, and operational efficiency for SDN fault and service management.	10.1109/TNSM.2026.3744288
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
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
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
Ting-Yu Chang, Cooper Cheng-Yuan Ku	Two-tier Framework with AutoML for Detection and Classification of Multiple Types of Anomalies in Intrusion Attacks	2026	Early Access	Modeling Optimization Probability	Ensuring cybersecurity is paramount for protecting sensitive data and defending against increasingly sophisticated cyberattacks. Consequently, intrusion detection (ID) has become a critical area of research in network security. This paper proposes a two-tier framework that leverages efficient automated machine learning (AutoML) techniques to address data imbalance and optimize feature combinations for ID and classification of multiple types of anomalies. In the first tier, two complementary models are selected: a primary model optimized for overall dataset performance and a secondary model specifically chosen for its effectiveness on balanced data distributions. In the second tier, a probability stacking approach, supported by Out-of-Fold (OOF) cross-validation, integrates insights from the first-tier models. It acts as a binary safety net that successfully recovers rare minority attacks while preventing irreversible information loss. The proposed framework effectively mitigates the adverse effects of data imbalance while requiring only a minimal runtime memory footprint of approximately 44 MB (NSL-KDD) and a total process footprint of 165.52 MB (CIC-IIoT-2025), and achieves a rapid cold-start initialization of 0.85 seconds. These advantages make it well-suited for edge computing. Experimental results on the NSL-KDD dataset demonstrate that the proposed approach achieves an overall accuracy of 99.73%, outperforming many state-of-the-art methods by 0.4-20.6%. Furthermore, evaluations on the CIC-IDS-2017 dataset show a 6.3% accuracy improvement and a significant 36% increase in macro-average recall compared with a random-forest-only (RF-only) model. Moreover, extensive validation on the contemporary CIC-IIoT-2025 dataset reveals a significant 4.20% improvement in Macro F1-score over up-to-date baselines. Finally, evaluations on an independent set with novel attack types (KDDTest+) confirm strong generalization capability, achieving 77.10% accuracy and underscoring the framework's robustness in modern industrial scenarios.	10.1109/TNSM.2026.3747311
Aleksandar Avdalović, Joseph Khoury, Ibrahem AlJabea, Mustafa Hajij, Elias Bou-Harb	Beyond Flat Models: Preserving ICS Topology using Combinatorial Complexes for Hierarchy-Aware Intrusion Detection	2026	Early Access	Modeling Cells (biology) Integrated circuits	Industrial Control Systems (ICS) follow the Purdue model hierarchy, organizing sensors, actuators, Programmable Logic Controllers (PLCs), and supervisory control into distinct hierarchical layers. Despite this inherent structure, existing data-driven intrusion detectors still fail to model this intricate property. To address this gap, we introduce a Hierarchy-Aware Intrusion Detector, HAID, a topology-preserving framework that models an ICS as a Combinatorial Complex (CC), operating solely on process telemetry and trained on benign traces to detect falsified sensor measurements and unauthorized actuator commands. The complex captures devices, functional interactions, and PLC control zones within an ICS. CCs (unlike flat models) encode telemetry across hierarchical levels for training a Topological Neural Network (TNN). As such, HAID enables the embedding of the ICS Purdue model into the learning process, yielding anomaly scores and localization at each of the aforementioned levels. Evaluated on two water testbeds, SWaT and WADI, and on a simulated chemical process, TEP, HAID achieves competitive detection performance (82.7% F1 on SWaT and 50.1% on WADI, averaged over five runs), while providing explicit hierarchical localization unavailable in prior work. Through ablations, we confirm the added-value of hierarchical modeling, as removing the PLC-zone representation reduces F1 by 5.8 percentage points on SWaT and causes detection on WADI to collapse. The largest losses are on attacks that manipulate several devices at once, which device- and interactionlevel signals usually miss.	10.1109/TNSM.2026.3746967
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
Qu Wang, Hao Wu, Xin Luo	A Highly Accurate Approach to Dynamic Network Representation Learning Based on Biased Nonnegative Tensor Ring Factorization	2026	Early Access	Modeling Tensors Ranking (statistics)	A large-scale dynamic network can be represented as a high-dimensional and incomplete (HDI) tensor, whose numerous entries are missing because it is impossible to acquire all interactions among massive numbers of nodes. Tensor network-based models have emerged as powerful tools for HDI tensor representation learning. Tensor ring (TR) decomposition represents a high-order tensor as a cyclic interconnection of third-order tensors, providing a compact tensor network structure for latent representation learning. However, conventional TR decomposition is designed for complete tensors and thus does not account for the substantial missing data in HDI dynamic networks, while existing TR-based representation learning models also have limited ability to model multidimensional link fluctuations. To address these issues, this work proposes a biased nonnegative tensor ring factorization (BNTRF) model with three main components: a) adopting a data-density-oriented modeling strategy that focuses computation on observed entries, thereby reducing the modeling cost of HDI tensors; b) designing a linear bias mechanism to capture multidimensional link fluctuations, thereby improving model stability and representation accuracy; and c) developing a self-adaptive parameter learning scheme to improve computational efficiency. Experimental studies on six real dynamic networks demonstrate that BNTRF outperforms several state-of-the-art models in learning accuracy while incurring lower computational costs.	10.1109/TNSM.2026.3748838
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
Martha Cash, Charlotte Fowler, Alexander M. Wyglinski	On the Role of Time Series Clustering in Traffic Matrix Prediction	2026	Early Access	Fluid flow Timing Modeling	This paper analyzes the role of time-series clustering in traffic matrix (TM) prediction. Traffic flows within a TM often exhibit heterogeneous behavior, which can reduce the effectiveness of global forecasting models that predict all flows jointly. To address this, we propose a clustering-based prediction framework that groups flows into smaller subsets and trains separate predictors for each group. We evaluate four traffic-flow representations for clustering, namely histogram, autocorrelation function (ACF), power spectral density (PSD), and naïve partitioning, and analyze how both the representation choice and the number of clusters affect prediction performance. Experiments using the publicly available Abilene and GÉANT datasets show that clustering consistently improves over global forecasting baselines, while remaining substantially less costly than local prediction. The results further show that most of the performance gain is achieved at moderate values of K, with diminishing returns as the number of clusters increases. Although different clustering representations produce different partitions of the traffic flows, they often achieve similar root mean squared error (RMSE). This suggests that the main benefit of clustering lies in decomposing the TM prediction task into smaller subproblems, while the exact cluster structure plays a more limited role in determining overall prediction accuracy.	10.1109/TNSM.2026.3747898
Diogo Pereira, Rodolfo Oliveira, Daniel Benevides Da Costa, Hyong Kim	Performance Assessment of Beamforming Training Protocols in IEEE 802.11ad/ay Networks	2026	Early Access	Modeling Probability Media Access Control	This paper introduces an innovative two-step methodology to model the beamforming training (BFT) medium access control (MAC) behavior of IEEE 802.11ad and IEEE 802.11ay networks, capturing several MAC-layer features specified in the standards that previous work has not considered. The proposed framework jointly employs two discrete-time Markov chains (DTMCs): the first models the individual behavior of each transmitting station (STA), including the dynamic adjustment of the competing STA to account for the idle period, while the second captures the network-wide interactions of all STAs and uses this information to derive the medium access probability at each frame slot during the BFT process. This work introduces two key novel modeling assumptions: (1) the STAs cease access once they successfully access a BFT frame’s slot, and (2) the number of competing STAs dynamically decreases as they enter the idle period after a given number of consecutive collisions, providing a precise representation of BFT MAC operation in IEEE 802.11ad and IEEE 802.11ay standards. We derive the theoretical expressions for the expected number of idle STAs based on probabilities of successful BFT, collisions, and transitions to the idle period.We validate the model’s accuracy through Monte Carlo simulations across diverse network scenarios, showing close alignment with simulation results and improved accuracy over prior models that do not consider these two modeling assumptions. Additionally, we analyze the impact of the collision threshold and idle period duration on BFT goodput, highlighting the model’s ability to compute MAC protocol performance.	10.1109/TNSM.2026.3751488
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
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
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
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	Local community detection multiple populations evolutionary algorithms	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
Rosario Patanè	Dynamic Coalition Formation Under Capacity Constraints: A Regime-Switching Value-Investing Game	2026	Early Access	Games Modeling Resource management	Edge computing service deployment requires the Network Operator (NO) and Service Providers (SPs) to coordinate investment and resource allocation under uncertain demand. This paper develops an audit-based service management framework for daily coalition formation with fixed capacity and service-level agreement (SLA) reservations. A two-stage coalitional game separates infrastructure investment from operational participation. Demand follows a Geometric Random Walk (GRW), and SPs apply an ordered admission and suspension rule using reported marginal contributions. With the NO held fixed, the operational coalition value is submodular; mandatory reservations can produce negative Shapley payoffs. We derive a separable benchmark, compute operational Shapley allocations, and give a sufficient condition for convergence to an individual equilibrium. A probabilistic audit mechanism provides a conditional daily truthfulness guarantee under verified settlement and enforceable penalties. For five service classes over three years, the reference run gains 12.4% over static grand-coalition membership and recovers 94.6% of the full-information optimum. Across 30 runs, the median gain is 13.3%. Sensitivity tests examine policy parameters, capacity and alternative demand models. The gain depends on capacity and update order; abundant capacity can make the admission rule underperform static membership.	10.1109/TNSM.2026.3750592