TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Arad Kotzer, Tom Azoulay, Yoad Abels, Aviv Yaish, Ori Rottenstreich	SoK: DeFi Lending and Yield Aggregation Protocol Taxonomy, Empirical Measurements, and Security Challenges	2026	Early Access	Filtering Application specific integrated circuits Filters	Decentralized Finance (DeFi) lending protocols implement programmable credit markets without intermediaries. This paper systematizes the DeFi lending ecosystem, spanning collateralized lending (including over- and under- collateralized designs, and zero-liquidation loans), uncollateralized primitives (e.g., flashloans), and yield aggregation protocols which allocate capital across underlying lending platforms. Beyond a taxonomy of mechanisms and comparing protocols, we provide empirical on-chain measurements of lending activity and user behavior, using Compound V2 and AAVE V2 as case studies, and connect empirical observations to protocol design choices (e.g., interestrate models and liquidation incentives). We then characterize vulnerabilities that arise due to notable designs, focusing on interestrate setting mechanisms and time-measurement approaches. Finally, we outline open questions at the intersection of mechanism design, empirical measurement and security for future research.	10.1109/TNSM.2026.3682174
Miguel Catalan-Cid, Joan Josep Aleixendri, Jorge Pueyo, Pau Tomas, Daniel Camps-Mur	COSMO: O-RAN-Based Service Management and Orchestration for Cross-Technology Multi-Tenant Radio Access Networks	2026	Early Access	Radio access networks Regional area networks 5G mobile communication	The evolution toward 6G networks envisions a heterogeneous Radio Access Network (RAN) comprising diverse access technologies, such as private 5G, public 4G/5G, and Wi-Fi, managed by multiple stakeholders. While considerable research effort has been devoted to O-RAN-based frameworks enabling rApp and xApp implementation and validation, few works provide integrated support for cross-technology RAN orchestration, end-to-end multi-tenancy, and a unified subset of SMO functionalities, including Non-RT RIC components. This paper introduces COSMO, a novel RAN Service Management and Orchestration platform designed to support heterogeneous 3GPP (5G NR, LTE) and non-3GPP (Wi-Fi) access networks. COSMO enables cross-technology multi-tenancy, defined as the capability to allow multiple tenants to dynamically share heterogeneous RAN resources with explicit resource allocation guarantees based on Service Level Agreements (SLAs). This is achieved through management primitives that support flexible and on-demand resource allocation. Additionally, the platform includes a cross-technology Non-Real-Time RAN Intelligent Controller (Non-RT RIC) that enables the development of intelligent rApps for closed-loop control and network orchestration. Beyond its architectural design, COSMO improves resource utilization and operational flexibility through unified orchestration of heterogeneous multi-tenant RAN resources. Through prototyping and benchmarking, we demonstrate the effectiveness of COSMO in resource allocation, SLA enforcement, and scalability. In our prototype, the SLA-based rApp reduces SLA violation from approximately 21% to below 10% under dynamic traffic conditions in a heterogeneous RAN deployment including 5G, 4G, and Wi-Fi access networks. Our results confirm that COSMO offers an efficient solution for managing and orchestrating future multi-tenant cross-technology RAN environments.	10.1109/TNSM.2026.3695361
Ramiz Sharafat Rajput, Xiaohan Wu, Shihe Xu, Xinming Zhang	QoE-Aware Multi-Representation Caching in Heterogeneous LEO Satellite–Edge Networks	2026	Early Access		In the platform of Low Earth Orbit (LEO) satellite constellations combined with terrestrial edge servers, both satellite and edge nodes have limited storage and computing resources, and each content item may be available in multiple bitrate–quality representations, making cache allocation a challenging task. In this paper, we study QoE-aware multi-representation content caching in a heterogeneous LEO satellite– edge architecture, where a serving LEO satellite cooperates with multiple edge servers to deliver encoded video representations to users. We model the expected user Quality of Experience (QoE) by combining a content popularity distribution with a representation-dependent quality metric and a hierarchical delivery path that prioritizes nearby edge caches, followed by serving satellites and inter-satellite links or gateways. We formulate cache placement decisions at satellite and edge nodes as a joint optimization problem under storage and computing constraints and show that the resulting utility function is monotone and submodular. This allows us to cast the problem as a submodular knapsack maximization and to design a greedy caching strategy that iteratively selects cache actions with the largest marginal QoE gain per unit resource cost. Simulation results show that the proposed approach yields higher QoE than conventional ones.	10.1109/TNSM.2026.3695712
Haoyu Luo, Ming Liu, Shaojian Qiu, Xiao Liu	FaaSAdapter: An Adaptive Resource Configuration Framework for Serverless Workflows at the Edge	2026	Early Access		Serverless computing has emerged as a promising deployment paradigm for edge scenarios, owing to its efficient resource utilization and flexible provisioning enabled by Function-as-a-Service (FaaS). In Serverless environment, developers are required to configure resources for functions to balance cost efficiency and performance. However, determining appropriate resource allocations for the functions running at the edge is a challenge due to the dynamic nature of the environment. This challenge is further compounded when managing serverless workflows composed of multiple interconnected functions with complex dependencies. To address such an challenge, we present FaaSAdapter, an efficient runtime resource configuration framework for workflow functions, aiming at conserving computational resources at the edge while ensuring timely response to user requests. Different from existing dynamic resource configuration methods that incrementally determine resource schemes for only the immediate subsequent workflow function, FaaSAdapter predicts the execution times of all the unexecuted functions across various resource configurations and determines an optimal configuration schema for the function instances based on the current execution progress. Then, it updates the configuration schema as needed during runtime. Comprehensive experiments demonstrate that FaaSAdapter ensures satisfactory response time of user requests with lowest resource consumption.	10.1109/TNSM.2026.3695591
Xiaomao Zhou, Zihao Shao, Qingmin Jia, Renchao Xie	ProxyLLM: Augmenting LLMs with Proxy Models for Tool Utilization in Network Service Generation	2026	Early Access	Tools Modeling Large language models	This paper introduces ProxyLLM, a novel framework designed to enhance the tool utilization capabilities of Large Language Models (LLMs) by leveraging an ensemble of smaller, specialized proxy models. Specifically, instead of invoking tools directly, ProxyLLM delegates tasks to these proxy models, each of which is responsible for a distinct domain and equipped with a curated set of relevant tools. Meanwhile, ProxyLLM employs a two-step knowledge transfer mechanism, utilizing data generated by the LLM for knowledge distillation and LLM-guided Deep Reinforcement Learning (DRL) to enhance the decision-making abilities of the proxy models. During the data-driven knowledge distillation process, the introduction of rationales ensures that proxy models maintain a comprehensive understanding of tasks, thereby improving the learning effectiveness. In the DRL learning process, LLM guidance is separately integrated into both the actor and critic learning phases. This ensures consistency in strategy and uniformity in evaluating the action space, which enhances both the efficiency and effectiveness of the learning process. Extensive experiments, including real-world applications such as network service generation in a Computing Power Network (CPN) system, demonstrate that ProxyLLM significantly outperforms existing methods in terms of task accuracy and tool invocation efficiency. The proposed framework offers a promising solution for constructing generalizable, large-scale intelligent agents capable of effectively leveraging diverse tools to solve complex, cross-domain problems.	10.1109/TNSM.2026.3695074
Jingyou Chen, Zhangfa Wu, Yi Hua, Hongqi Li, Yilei Shi, Hongping Gan	Self-contrastive Learning to Boost Weakly Supervised Anomaly Detection	2026	Early Access	Anomaly detection Modeling Labeling	Weakly supervised anomaly detection methods (WADMs) can effectively utilize incomplete label data to address the issue of imbalanced samples, thereby reducing the reliance on the quantity of labeled data and demonstrating superior anomaly detection capabilities in network and service management. However, when confronted with significant label noise and missing labels, existing WADMs still struggle to adequately extract the deep feature information of samples, leading to a decline in model performance. To tackle this challenge, we propose a self-contrastive enhanced weakly supervised anomaly detection framework, called SEAD-Net, which enhances the feature representation capability of data in the model’s feature space, thereby improving the accuracy and robustness of anomaly detection. Specifically, we first design a personalized data enhancement module that augments data representation by applying various transformations to the raw data. Subsequently, a self-contrastive enhanced learning module is introduced to impose hybrid constraints on the augmented samples, constructing the overall distribution structure while learning deep sample feature spaces under complex scenario disturbances. Finally, we extract contrastive enhancement features within the deep sample feature space and perform probabilistic generation to enable effective decision-making via an anomaly probability generation module. Experimental results on a series of public benchmark datasets demonstrate that our SEAD-Net outperforms the second-best WADM by 5.95% in average AUC-ROC and 16.28% in average AUC-PR.	10.1109/TNSM.2026.3694683
Arman Sanaei, Massoud Reza Hashemi	Adaptive, Profit-Aware RAN Slicing for Multi-Operator Networks via Spatio-Temporal Prediction and Energy-Aware SAC	2026	Early Access	Cells (biology) Energy Modeling	The proliferation of heterogeneous 5G/6G services and the emergence of multi-tenant deployments have made isolation-preserving RAN slicing a central requirement for shared infrastructures. This paper studies multi-tenant RAN slicing where multiple Mobile Virtual Network Operators (MVNOs) compete for a common pool of radio and edge-compute resources and must maximize long-term economic performance while preserving slice isolation. We propose a two-timescale framework that couples (i) prediction-driven, per-cell Long Time Slot (LTS) reservation with an isolation-aware congestion/pressure cost that coordinates competing MVNO demands, and (ii) Short Time Slot (STS) per-user allocation modeled as a Markov decision process and solved via an energy-aware Soft Actor–Critic (SAC) policy. This design separates strategic capacity planning from fast, stochastic user-level control, while retaining tractability under dynamic traffic, mobility, and channel uncertainty. Extensive simulations across small- and large-scale deployments show that the proposed approach improves MVNO profit per cell per LTS by up to 26% over representative baselines, while maintaining robust isolation under asymmetric demand and rival-tenant growth.	10.1109/TNSM.2026.3694799
Ashely Li, Jeffrey Chang, Steven S. W. Lee	Modeling and Optimization Algorithm for Capacity Planning in Hose Model VPN Networks	2026	Early Access	Joining processes Modeling Algorithms	Hose-based VPNs offer greater bandwidth flexibility, as they allow traffic to and from a hose endpoint to be arbitrarily distributed across other endpoints. Existing studies on hose-based VPNs have primarily focused on VPN provisioning algorithms, while optimal capacity planning for hose-based VPN networks remains largely unexplored. Given budget constraints and forecasts of future bandwidth demands at VPN endpoints, the capacity planning problem requires the joint optimization of routing decisions and link capacity allocation. Although the problem can be formulated as a nonlinear programming model, its nonconvex nature makes direct solution computationally challenging. To address this issue, we reformulate the problem as a sequence of linear programming problems and develop a solution framework based on a water-filling algorithm. For any defined budget and relative tolerance, the proposed algorithm yields a near-optimal solution where the network expansion cost stays within the allowed margin. Numerical results demonstrate that the proposed approach efficiently solves the hose-based VPN capacity planning problem within practical computation time.	10.1109/TNSM.2026.3694390
Anum Lodhi, Xiong Li, Muhammad Asad Saleem, Muhammad Ali Lodhi, Khalid Mahmood, Salman Shamshad	DroneSec: Efficient and Secure Communication for Resource-Constrained Drones in IoD Systems	2026	Early Access	Protocols Drones Authentication	The Internet of Drones (IoD) represents an emerging paradigm of the Internet of Things (IoT), enabling seamless, coordinated communication among drones and integration with other connected systems. This interconnected network enables autonomous decision-making among drones. As this IoD paradigm continues to expand it faces significant challenges due to its reliance on public channel. Therefore, existing methods often suffer from impersonation, cloning, anonymity violation, and fails to offer end-to-end key secrecy. Moreover, they require high computation resources which present challenges of deployment in resource-constrained IoD environment. To address these challenges, we propose a secure and efficient protocol that provides mutual authentication among participating entities. The protocol resists impersonation, cloning, anonymity violation and offers end-to-end key secrecy. The protocol employs Physical Unclonable Function (PUF) and Elliptic Curve Cryptography (ECC), along with a fuzzy extractor, to ensure secure communication. The resilience of the proposed protocol was evaluated through an informal analysis. Its security properties were rigorously verified using formal analysis based on the Real-Or-Random (ROR) model. Evaluation of its performance shows that the proposed protocol outperforms existing solutions, achieving reductions of 20.9% in computation cost and 32.6% in communication overhead. The results demonstrate that the protocol improves security and provides reliability under the evaluated scenarios of IoD systems.	10.1109/TNSM.2026.3694492
Lizhuang Tan, Nguyen Van Tu, Xinhang Wang, Peiying Zhang, James Won-Ki Hong	SDNIE: A Software-Defined Approach to High-Performance Network Impairment Emulation using Programmable Switches	2026	Early Access	Emulation Central Processing Unit Testing	Network testing is critical for evaluating the performance, reliability, and security of modern computer networks. A key challenge is creating an accurate, cost-effective, and high-performance network emulation environment. Network Impairment Emulators (NIEs) emulate real-world network conditions such as bandwidth constraints, latency, and packet loss, but existing CPU- and FPGA-based solutions suffer from limited performance, high costs, and poor flexibility. This paper proposes Software-Defined Network Impairment Emulation (SDNIE), a novel framework that leverages programmable switches for scalable, cost-efficient network impairment emulation. SDNIE introduces three key techniques: (1) intent-driven network impairment configuration, automating impairment modeling; (2) serial-parallel combined execution, optimizing performance; and (3) CPU-Tofino collaborative deployment, offloading complex computations. Experimental results show that SDNIE matches commercial emulators in performance while significantly reducing costs. This work demonstrates the potential of programmable switches in network testing, offering a scalable, cost-effective, and high-performance alternative for next-generation network impairment emulation.	10.1109/TNSM.2026.3694388
Elie Inaty, Charbel Maroun, Ghattas Akkad, Ali Mansour, Martin Maier	A 6G Driven Multiclass Power Efficient Dynamic Bandwidth Allocation (MPE-DBA) Scheme for Passive Optical Network (PON)	2026	Early Access	Costing Costs Algorithms	The latest ITU IMT-2030 recommendations for sixth generation (6G) networks have imposed strict specifications on communication systems. Some of these requirements include increased throughput, ultra-low delay and jitter, differentiated services, and energy efficiency. Current dynamic bandwidth allocation (DBA) schemes for passive optical networks (PON) may meet some of these requirements, yet they fail to fulfill other recommendations, especially energy efficiency. Therefore, we propose a new PON architecture, whose objective is to offer flexibility in meeting the IMT-2030 recommendations. It uses a multiclass power efficient dynamic bandwidth allocation (MPE-DBA) scheme that helps achieving both differentiated services and sustainability in terms of energy and cost. For computational efficiency, we propose a two stages Mamdani fuzzy inference system (FIS). The inputs of the first FIS are the latency and cost of the 6G traffic, whereas the latency and cost of the non-6G (N6G) traffic are the inputs of the second FIS stage. Both FISs use the variation in the number of channels as output. The proposed algorithm achieves less than 100 μs delay, less than 10μs jitter and high aggregate throughput for the 6G packets. In addition, it reduces the power consumption by three times and the cost of traffic transmission by four times as compared to the state-of-the art solution.	10.1109/TNSM.2026.3694150
Jiang Mo, Ke Zhao, Limei Peng, Hsiao-Chun Wu	PDO-SFCM: Prediction-Driven Orchestration for SFC Migration in SAGIN via Fine-Tuned Large Time-Series Model and DRL	2026	Early Access	Modeling Space-air-ground integrated networks Timing	Space-air-ground integrated networks (SAGINs) have emerged as an appealing enabling technology for the next-generation ubiquitous connectivity. By extending terrestrial networks with aerial and space platforms, SAGIN can provide seamless coverage and flexible resource-access across various altitudes. However, dynamic link conditions, intermittent connectivity, and heterogeneous latency constraints would often introduce serious challenges to the service function chain (SFC) migration and orchestration. In this work, we introduce a novel PDO-SFCM (prediction-driven orchestration for SFC migration) approach, which utilizes a fine-tuned large time-series model (LTM) for network status prediction and a deep reinforcement learning (DRL) module for proactive SFC migration in SAGINs. In detail, the fine-tuned LTM predicts multi-horizon estimates of SFC arrivals and per virtual network function (per-VNF) resource demands, which will form the observation space of the DRL agent. The DRL module thus schedules appropriate migration actions on the cost-augmented time-expanded graph (C-eTEG), which can satisfy the feasibility subject to the bandwidth, buffering, and precedence constraints. Extensive simulation results demonstrate that our proposed new PDO-SFCM scheme consistently greatly improves the acceptance rate, reduces the end-to-end delay, and lowers the migration cost in comparison with DRL baselines under different prediction settings. Our proposed new scheme can significantly leverage the SAGIN performance by the devised foundation-level time-series prediction and learning-based orchestration mechanisms.	10.1109/TNSM.2026.3694203
Xingyu He, Nianci Li, Panxing Huang, Chunhua Gu, Guisong Yang, Yunhuai Liu	Dynamic Spatiotemporal Dual-Encoder Transformer for Long-Term Traffic Prediction in LEO Satellite Networks	2026	Early Access	Satellites Modeling Low earth orbit satellites	Accurate long-term traffic prediction in Low Earth Orbit (LEO) satellite networks is essential for proactive resource allocation and congestion avoidance, yet remains challenging due to highly dynamic topologies, intermittent connectivity, and scarce real traffic data. Existing approaches are largely limited to short-term prediction or assume static spatial dependencies, making them inadequate for non-stationary LEO environments. To address these challenges, this paper proposes DST-DEformer, a dynamic spatial–temporal Transformer framework that jointly models evolving inter-satellite topology and multi-scale temporal dependencies. Specifically, a topology-adaptive graph convolution module captures time-varying spatial correlations, while a dual temporal encoder decouples long-term global trend modeling from short-term local fluctuation learning. In addition, a hybrid simulation–calibration framework is developed to generate realistic satellite traffic by incorporating orbital dynamics, demographic information, and real-world traffic trends. Extensive experiments on simulated LEO satellite traffic and the PEMS08 benchmark show that DST-DEformer consistently outperforms state-of-the-art methods in long-term prediction, achieving 4%-13% reductions in MSE and MAE and significantly slower error accumulation as the prediction horizon increases. These results demonstrate the effectiveness and robustness of DST-DEformer for long-term traffic prediction under dynamic network topologies.	10.1109/TNSM.2026.3693648
Dinghao Zeng, Fagui Liu, Runbin Chen, Jingwei Tan, Dishi Xu, Qingbo Wu, C.L. Philip Chen	CoreScaler: A Resource-Efficient Hybrid Scaling Framework for Dynamic Workloads in Cloud	2026	Early Access	Resource management Central Processing Unit Memory	Containerized microservices face significant challenges in balancing service quality and resource efficiency under dynamic workloads. Existing approaches suffer from horizontal scaling’s cold start latency, vertical scaling’s resource ceilings, and hybrid methods’ limited adaptability. We present CoreScaler, a resource-efficient hybrid scaling framework based on analysis of CPU usage patterns revealing substantial consumption differences between working mode and waiting mode instances. This insight drives our dual-mode instance management model that distinguishes between working instances actively handling requests and waiting instances maintaining hot standby with minimal resource allocation. CoreScaler employs a master-subordinate distributed architecture where the master node performs capacity planning using multi-confidence interval predictions and contextual multi-armed bandit optimization, while subordinate nodes execute mode-aware CPU quota adjustments. Comprehensive evaluation on a Kubernetes cluster with a typical microservice system under four representative production work-loads demonstrates that CoreScaler maintains SLO compliance while reducing CPU and memory allocation by 22.53% and 30.83% respectively compared to state-of-the-art solutions. The framework achieves substantially higher resource utilization than single-dimension scaling approaches, validating the effectiveness of coordinated hybrid scaling for dynamic cloud environments.	10.1109/TNSM.2026.3692955
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
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
Songshou Dong, Yanqing Yao, Huaxiong Wang, Yining Liu	LCMS: Efficient Lattice-based Conditional Privacy-preserving Multi-receiver Signcryption Scheme for Internet of Vehicles	2026	Early Access	Optical waveguides Optical fibers Broadcasting	Internet of Vehicles (IoV) requires robust security and privacy protection mechanisms to enable trusted traffic information exchange, while also requiring low communication and low computing overhead to meet the real-time requirements of IoV. Existing signcryption schemes suffer from quantum vulnerability, inadequate unlinkability/vehicle anonymity, absence of revocability, poor scalability, inadequate management of malicious entities, and high communication and computational overhead. So we propose an efficient lattice-based conditional privacy-preserving multi-receiver signcryption scheme (LCMS) that systematically addresses these gaps through three core innovations: 1) Privacy preservation is achieved via a pseudonym mechanism integrated with certificateless key generation, which ensures vehicle anonymity and weak unlinkability while preventing malicious key generation center and key escrow; 2) Malicious entity management through dynamic revocability and distributed decryption among roadside units, preventing unilateral message access; and 3) Post-quantum efficiency is achieved by leveraging the Learning With Rounding problem to eliminate expensive Gaussian sampling, combined with ciphertext packing techniques. This reduces time overhead, the size of signcryptexts, and communication overhead, while lowering the overall storage overhead of the scheme through the MP12 trapdoor. Security proofs show LCMS achieves Existential Unforgeability under Adaptive Identity Chosen-Message Attack and Indistinguishability under Adaptive Identity Chosen-Ciphertext Attack in the Random Oracle Model, with rigorously validated resistance against multiple IoV-specific attacks. Experimental results via SageMath implementation demonstrate that our scheme exhibits a smaller signcryptext size and lower signcryption/unsigncryption time compared to existing random lattice-based signcryption schemes. Scalability tests with 300 vehicles and 300 roadside units (RSUs) were completed within 230 seconds. Communication overhead analysis confirms practical feasibility for IEEE 802.11p vehicle communication protocol, and RSU serving capability evaluation under realistic vehicle density (100–200/km2) and speed (40–60 km/h) further validates system practicality. LCMS provides a quantum-resistant, privacy-preserving, and efficient solution for production IoV.	10.1109/TNSM.2026.3688507
Awaneesh Kumar Yadav, Madhusanka Liyanage, An Braeken	An Improved and Provably Secure EDHOC Protocol Supporting the Extended Canetti–Krawczyk (eCK) Security Model	2026	Early Access	Aerospace and electronic systems Telemetry Central Processing Unit	Transport Layer Security (TLS) is considered to be the most used standard security protocol for the Internet of Things (IoT). However, as TLS was originally designed for computer networks, it is not optimal with respect to efficiency. Therefore, a new protocol called Object Security for Constrained RESTful Environments (OSCORE) has been standardized for securing constrained devices. Currently, the Ephemeral Diffie Hellman Over COSE (EDHOC) protocol, which is a key exchange protocol to define a session key used in OSCORE, is also in the process of being standardized. This paper shows that the four authentication modes of the EDHOC protocol are vulnerable in the extended Canetti–Krawczyk (eCK) security model, which is a common security model used in IoT. In addition, also resistance to Distributed Denial of Service (DDoS) attacks is weak. Taking this into account, we propose two new variants of EDHOC. The first variant, EDHOC2, is able to overcome both issues but has a slightly higher cost for communication, computation, storage, and energy consumption. The second variant, EDHOC3, offers only additional protection in the eCK security model and has, on average, similar, even better performance in one authentication mode, compared to EDHOC. Additionally, the Real-Or-Random (ROR) logic and Scyther validation tool are employed to ensure the security of the designed variants. Furthermore, a prototype implementation is conducted to demonstrate the real-time deployment of the designed versions.	10.1109/TNSM.2026.3690530
Jan Luxemburk, Karel Hynek, Richard Plný, Tomáš Čejka	Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success	2026	Vol. 23, Issue	Transfer learning Training Cryptography	Encrypted traffic classification (TC) methods must adapt to new protocols and extensions as well as to advancements in other machine learning fields. In this paper, we adopt a transfer learning setup best known from computer vision. We first pretrain an embedding model on a complex task with a large number of classes and then transfer it to seven established TC datasets. The pretraining task is recognition of SNI domains in encrypted QUIC traffic, which in itself is a challenge for network monitoring due to the growing adoption of TLS Encrypted Client Hello. Our training pipeline—featuring a disjoint class setup, ArcFace loss function, and a modern deep learning architecture—aims to produce universal embeddings applicable across tasks. A transfer method based on model fine-tuning surpassed SOTA performance on nine of ten downstream TC tasks, with an average improvement of 6.4%. Furthermore, a comparison with a baseline method using raw packet sequences revealed unexpected findings with potential implications for the broader TC field. We released the model architecture, trained weights, and codebase for transfer learning experiments.	10.1109/TNSM.2025.3642984
Ioannis Dimolitsas, Maria Diamanti, Stefanos Voikos, Symeon Papavassiliou	Resilient RAN Selection and SFC Deployment in Dependable Wireless Edge Cloud Networks	2026	Vol. 23, Issue	Resource management Delays Wireless communication	The evolution toward sixth-generation (6G) networks necessitates integrated resource management solutions to address the interdependencies between network segments, such as Radio Access Network (RAN) and Edge Cloud (EC) infrastructures. Unified management of network and compute fabrics is crucial for achieving seamless service delivery, end-to-end power efficiency, and delay guarantees, while resiliency becomes a key enabler for adapting to various application demands and diverse network segment conditions. In this context, this paper proposes a unified framework for dependable wireless EC networks that jointly addresses the problems of RAN selection and Service Function Chain (SFC) embedding to minimize the total power consumption across network segments under end-to-end delay SFC deployment constraints. The framework iteratively solves these problems, considering the interdependencies between RAN ingress points and the EC network resource constraints. To deal with the high dimensionality of the considered parameters and achieve timely and scalable decision-making, a coalition formation game optimizes RAN selection, while a delay-aware heuristic approach undertakes the power-efficient embedding of multiple SFCs within the EC network. Simulation results demonstrate the framework’s efficiency in reducing power consumption compared to segment-specific approaches, highlighting the importance of cross-segment dependencies. Also, the adaptability of the proposed unified modeling and the framework’s scalability are demonstrated, ensuring resilient performance under varying network parameter settings.	10.1109/TNSM.2025.3645449