TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
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
Anselme Ndikumana, Kim Khoa Nguyen, Oscar Delgado, Adel Larabi, Mohamed Cheriet	Empowering Rural Areas with Energy-Efficient 5G IAB-Based Fixed Wireless Access Network	2026	Early Access	Resource management 5G mobile communication Modeling	Fixed Wireless Access (FWA) has recently emerged as a cost-effective alternative to optical fiber in rural areas, particularly where fiber deployment is economically infeasible. To extend coverage and increase capacity, FWA networks have begun to integrate Integrated Access and Backhaul (IAB) with mid- and high-band spectrum. However, the energy consumption of multi-hop IAB networks scales significantly with the number of hops, a challenge that prior research has not adequately addressed. This paper proposes an energy-efficient framework that minimizes network energy consumption by maximizing Resource Block (RB) utilization while avoiding both over- and under-allocation in multi-hop IAB-based FWA deployments. The proposed method jointly allocates RBs and selects modulation and coding schemes across a mixed set of 5G numerologies to satisfy data rate requirements while minimizing energy consumption. The inherent dynamic interactions among IAB stations render the problem highly complex and non-convex; therefore, we design a disciplined multi-convex programming supported by dynamic programming algorithms to obtain tractable solutions. Furthermore, we introduce a transformer-based prediction to forecast RB distribution, thereby mitigating the need for frequent short-timescale coordination among IAB stations. Our simulation results demonstrate that the proposed approach achieves the required data rates while reducing energy consumption by 14%.	10.1109/TNSM.2026.3719631
Jheng-Jia Huang, Guan-Yu Chen, Hiroaki Kikuchi, Po-Yuan Su	Full-duplex Low-latency Handover and Transmission Authentication Protocol for 6G Networks	2026	Early Access	Protocols Authentication Modeling	Sixth-generation (6G) networks demand ultra-low latency, high reliability, and seamless handover. However, existing authentication and handover mechanisms, such as 3GPP 5G-AKA/EAP-AKA’ and Xn-/N2-based procedures, are based on half-duplex request–response signaling and incur sequential waiting delays. We propose a full-duplex–aware authentication and handover framework empowered by Co-frequency Co-time Full Duplex (CCFD) communication. By reorganizing 3GPP-style signaling so that authentication, key updates, and connection setup proceed in parallel between the UE, serving gNB, and target gNB, the central cloud is kept outside the real-time handover path. We formalize a game-based security model and prove mutual authentication under a pseudorandom permutation assumption. At the protocol level, we combine measured cryptographic costs with a link-delay model to compare the proposed scheme with the 3GPP baseline and representative academic protocols. The results show that full-duplex signaling can significantly reduce authentication and handover latency while preserving comparable security guarantees.	10.1109/TNSM.2026.3720523
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
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
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
Wei Sai, Yihui Lu, Xin Guo	A Privacy-Preserving Security Framework for Multi-Party Data Fusion Computing Based on Homomorphic Encryption	2026	Early Access	Security Protocols Information rates	To prevent plaintext exposure in multi-party collaborative computing, this paper proposes a distributed secure multi-party computation protocol based on the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme. Data is encoded and encrypted at the source into CKKS complex polynomial ciphertext, enabling vectorized fusion under shared evaluation keys and threshold decryption in a decentralized architecture without a trusted central authority. Experiments on heterogeneous multi-institution datasets demonstrate low numerical error (9.0×10⁻⁷ at polynomial order 2¹⁶ and depth 12), effective scalability (throughput increasing from 1.12×10⁵ to 1.32×10⁵ ops/s and latency decreasing from 56 ms to 38 ms as nodes scale from 4 to 16), and strong robustness (70% decryption success at a 60% threshold and 95% recovery under malicious interference), showing that the framework achieves efficient computation with strict privacy protection for cross-party data fusion.	10.1109/TNSM.2026.3717343
Yang Wu, Xu Cheng, Wenguang Zheng, Yingyuan Xiao	A Geo-Aware Personalized Network for User and Service Representation and Bilinear Interaction Modeling in QoS Prediction	2026	Early Access	Modeling Quality of service Matrices	With the rapid growth of the Internet, the proliferation of functionally similar web services has made Quality of Service (QoS) prediction, which measures service performance, increasingly critical. In QoS prediction, the QoS values observed from user-service invocations are often significantly affected by their geographical location. However, existing QoS prediction methods typically assume static user and service representations, overlooking geographic differences. We argue that even the same user or service should have personalized representations based on different geographic locations. To address this, we propose GeoPerNet, a Geo-Aware Personalized Network for QoS Prediction. Specifically, we design the Geographical Aware Personalization Module, which models the geographical similarity between users and services to select the most relevant top-k neighbors for the target user or service. We then apply geographic similarity-based weighting to highlight key neighbor information. Next, we leverage the designed GeoTransformer to model the complex dependency relationships among neighbors. Finally, the refined neighbor representations are fused with the original embeddings to generate personalized user and service representations. Additionally, we design the Bilinear Interaction Module to capture fine-grained interaction relationships between users and services using a bilinear function. Experiments on the large-scale WS-DREAM dataset demonstrate that GeoPerNet outperforms state-of-the-art approaches.	10.1109/TNSM.2026.3719697
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
Alessandro Buratto, Marco Levorato, Leonardo Badia	DCP: a TCP-Inspired Domain Adaptation in Dynamic Data Drift	2026	Early Access	Modeling Timing Costing	Mobile devices are affected by computing limitations, battery life, and connectivity issues, making it difficult to execute complex machine learning models or frequently transmit data. These challenges hinder real-time adaptability in dynamic environments. To tackle these issues, we introduce a framework between mobile devices and edge servers, where the edge server assists mobile devices by continuously fine-tuning a lightweight classifier to keep up with changes in data patterns. Our approach, called the Drift Control Protocol (DCP), is inspired by how TCP manages network congestion. Just as TCP interprets packet loss as network congestion and throttles transmission, DCP treats spikes in classification error as congestion and implements an additive increase multiplicative decrease mechanism to dynamically control the frequency of model retraining, optimizing the trade-off between the classifier update rate and the communication overhead with the edge server. It dynamically adjusts the update frequency based on how the data distribution shifts and controls how many samples the mobile device sends to the edge server, while improving overall accuracy. We test different versions of DCP on both synthetic and real-world datasets. Our results show that DCP procedures obtain better tradeoffs in mean error and communication costs when compared with constant interval updates policies.We demonstrate this capability across real-world data and parametrized synthetic datasets explicitly designed to simulate both bursty and gradual drift scenarios.	10.1109/TNSM.2026.3719234
Masoumeh Safkhani, Mohammad Reza Servati, Fatemeh Rezaei	HEIoT: A Novel Three-Factor Authentication Protocol for Enhanced Security in IoT and Next-Generation Networks	2026	Early Access	Authentication Internet of Things Protocols	The Internet has a significant impact on contemporary society, enabling a wide range of applications, including advanced cellular networks such as 4G, 5G, and 6G. Since these communications occur over shared or open channels, ensuring secure data exchange is of critical importance, as any weakness in the communication infrastructure may compromise system reliability. Device authentication in the Internet of Things (IoT) and user authentication in smart environments, such as smart homes, remain fundamental security challenges. As the first line of defense, authentication mechanisms must be robust, since vulnerabilities at this stage can expose the entire system to serious threats. To address these challenges, numerous authentication schemes based on cryptographic primitives, including Elliptic Curve Cryptography (ECC), have been proposed. In this paper, we present a comprehensive security analysis of an ECC-based three-factor authentication protocol proposed by Yuan et al. Our analysis shows that the protocol is vulnerable to desynchronization, user impersonation, traceability, and insider attacks, all of which succeed with probability 1 by exploiting at most two protocol phases. To mitigate these weaknesses, we propose an improved authentication scheme, called HEIoT. The proposed scheme is formally analyzed under the Real-or-Random (RoR) model to establish session-key security and is further verified using the Scyther tool. Moreover, a Python-based implementation is provided to demonstrate the practicality of the proposed protocol. Comparative results indicate that HEIoT achieves stronger security while maintaining acceptable communication, computational, and storage overhead.	10.1109/TNSM.2026.3702041
Jing Mei, Jinglei Xu, Zhao Tong, Keqin Li	Energy-Aware Multi-UAV Collaboration for Data Collection and Trajectory Planning with MADDPG	2026	Early Access		Unmanned Aerial Vehicles (UAVs) are pivotal for facilitating data collection in emergency scenarios. Despite the potential of Multi-Agent Deep Reinforcement Learning (MADRL) in coordinating such systems, existing researches struggle to resolve the high-dimensional coupling of data collection, trajectory planning, and energy scheduling under strict collision avoidance and Return-To-Base (RTB) constraints. This paper proposes a energy-aware cooperative MADRL framework designed to maximize data collection utility under energy constraints. Specifically, we employ a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) approach featuring a Centralized Training with Decentralized Execution (CTDE) design and a multi-objective reward mechanism to balance conflicting optimization goals. Extensive simulations validate the advantages of the proposed framework over leading baselines. Notably, the algorithm exhibits significant quantitative advantages in complex high-load scenarios. These outcomes prove that our method achieves higher task completion rates while strictly adhering to RTB and safety protocols.	10.1109/TNSM.2026.3721502
S A Harish, S Vignesh, Divya Pathak, Anil Kumar Sharma, Praveen Tammana	Anomaly Detection in In-Network Fast ReRoute Systems	2026	Early Access	Fluid flow Planing Delays	High-speed programmable data planes provide opportunities to implement data-driven fast reroute systems that quickly adapt to varying network conditions (e.g., congestion, failures) and improve network performance. The core of these systems has packet-processing algorithms running in the data plane that continuously look for traffic patterns (e.g., too many retransmissions) specific to a network condition (e.g., link failure) and take appropriate action (e.g., reroute). Despite their benefits, they also increase the potential attack surface. Adversaries can generate malicious traffic patterns resembling those anticipated by a fast reroute system and trick the system. Doing so would lead to poor network performance due to incorrect reroute decisions. In this paper, we propose a mechanism to detect whether the fast reroute systems are under the influence of malicious traffic patterns. Our key idea is to model the expected behavior using benign traffic features and use the model as a reference to determine whether the system is under the influence of adversaries. Using realistic attack traces, we demonstrate attacks on two fast reroute systems and successfully detect those attacks using the proposed detection mechanism.	10.1109/TNSM.2026.3715353
Qing Wu, Xijia Dong, Leyou Zhang, Yue Lei, Zilong Yan	Cloud-Assisted Verifiable and Updatable Private Set Union Protocol for Enhancing Network Intrusion Detection	2026	Early Access	Protocols Clouds Security	As network intrusion detection systems (NIDS) play an increasingly critical role in large-scale network environments, multiple organizations, Internet Service Providers (ISPs), and security service providers often maintain independent IP blacklists. Due to the dynamic nature of malicious IP addresses and their cross-organizational propagation, inter-organizational blacklist sharing is essential for improving network intrusion detection. However, traditional blacklist exchange mechanisms risk exposing participants’ complete blacklist information, and curious organizations may infer another organization’s detection strategies from the shared IP intersection, leading to privacy breaches.To address this issue, this paper proposes a Cloud-Assisted Verifiable and Updatable Private Set Union (CVU-PSU) protocol, which leverages the multi-query Reverse Private Membership Test (mq-RPMT) protocol and Oblivious Transfer (OT) technology to ensure privacy-preserving inter-organizational blacklist sharing. The protocol utilizes cloud computing to reduce the computational and communication overhead of participants in the mq-RPMT protocol while incorporating a verification mechanism to ensure the correctness of the cloud’s returned results. Furthermore, the protocol supports real-time blacklist updates, enabling adaptation to rapidly changing malicious IP addresses.Experimental results demonstrate that the proposed protocol achieves efficient inter-organizational blacklist sharing with low communication and computational costs while preserving privacy, thereby enhancing the real-time performance and accuracy of network intrusion detection systems.	10.1109/TNSM.2026.3716071
Ping He, Yu Yao, Xu Li, Yao Hu, Wei Yang	FIGAN: Diversity-Oriented Traffic Generation for Industrial Protocol Format Inference	2026	Early Access	Protocols Modeling Fuzzing	Protocol Format Inference is a pivotal step in the reverse engineering of proprietary protocols, yet its effectiveness is constrained by the scarcity of high-quality training data. In industrial control systems, the rigid and cyclical nature of traffic results in a "long-tail" distribution, where diverse functional scenarios are severely underrepresented. Existing generative approaches, primarily designed for fuzzing or intrusion detection, fail to resolve the intrinsic conflict between syntactic validity and semantic diversity required for protocol format inference. To bridge this gap, we propose FIGAN, a stage-wise decoupled generative framework tailored to synthesize high-fidelity traffic for protocol format inference. By isolating flexible distribution learning from rigid syntax enforcement, FIGAN liberates the generative process to extrapolate novel payload variations from a continuous latent space, effectively surmounting the limitations of sparse seed data. Specifically, the framework integrates three synergistic modules: first, heuristic pre-processing that constructs semantic templates as a prior knowledge base; second, a generative adversarial architecture optimized via discrete relaxation to explore high-dimensional payload patterns independently of syntax rules; and finally, a closed-loop verification mechanism that performs syntactic calibration and functional validation against simulated device responses. Evaluations on four real-world protocols (Modbus TCP, S7Comm, Omron FINS, and DNP3) demonstrate that FIGAN significantly outperforms state-of-the-art baselines. The source code has been open-sourced https://github.com/MissHP111/FIGAN.	10.1109/TNSM.2026.3717268
Xi Liu, Jun Liu, Weidong Li	Strategy-Proof Cost-Sharing Mechanism for Dynamic Adaptability Service in Vehicle Computing	2026	Vol. 23, Issue	Costs Sensors Vehicle dynamics	Vehicle computing has emerged as a promising paradigm for delivering time-sensitive computing services to Internet of Things applications. Intelligent vehicles (IVs) offer onboard computing and sensing capabilities for delivering a wide range of services. In this paper, we propose a dynamic adaptability service model that leverages the swift mobility of vehicles to adjust the distribution of IVs to users’ dynamically changing locations. There are two types of areas in our model: the user area and the parking area. The former is where services are provided, while the latter serves as the preparation zone for backup IVs. IVs in the parking area are dispatched to service areas, where existing vehicle resources cannot meet user demand, and they return to the parking area after delivering the service. Multiple users share sensing resources, and our model allocates the costs among them. To ensure strategy-proofness, we introduce the concepts of no additional cost and allocation stability. We propose a strategy-proof cost-sharing mechanism for dynamic adaptability service. The proposed mechanism achieves no positive transfers, voluntary participation, individual rationality, consumer sovereignty, budget balance, no additional costs, and allocation stability. Moreover, the proposed mechanism’s approximation performance is analyzed. We further use comprehensive simulations to verify the effectiveness and efficiency of the proposed mechanism.	10.1109/TNSM.2025.3646778
Fátima Khan, Óscar Gil, Luis Diez, Elena Serna Santiago, Luis M. Contreras, Ramón Agüero	Evaluating Fronthaul Network Performance Under the O-RAN Paradigm: A Novel Methodology Based on Queuing Theory	2026	Vol. 23, Issue	Delays 5G mobile communication Open RAN	Open Radio Access Network (O-RAN) fosters a new networking paradigm that supports flexible and open architectures, while enabling the functional disaggregation of traditional, monolithic, base stations. Specifically, the Lower-Layer Split (LLS) option 7.2x divides the PHY layer into Low-PHY at Open - Radio Unit (O-RU) and High-PHY at Open - Distributed Unit (O-DU), leveraging the so-called open fronthaul network, which imposes stringent latency requirements. In this work, we introduce and thoroughly examine the time window relationships that define the latency budget allocated to the open fronthaul network. Additionally, we propose a novel methodology to analyze the delay experienced by a realistic traffic pattern over a spine-leaf topology, accounting for various O-DU locations and site pools. To address the increased traffic load introduced by the fronthaul, we present a theoretical model to characterize the average delay across different priority levels when open fronthaul traffic coexists with flows from other Radio Access Technologies (RATs). This model is then validated and broadened through ns-3 simulations. Our findings show the applicability of the proposed methodology, which could be exploited by network operators in the planning phase, evincing its suitability to obtain benchmark delay values, offering a reliable tool for performance evaluation in open fronthaul networks.	10.1109/TNSM.2025.3646845
Imtiaz Ali Soomro, Hamood Ur Rehman Khan, Syed Jawad Hussain, Adeel Iqbal, Waqas Khalid, Heejung Yu	SecureDyn-FL: A Robust Privacy-Preserving Federated Learning Framework for Intrusion Detection in IoT Networks	2026	Vol. 23, Issue	Internet of Things Accuracy Robustness	The rapid proliferation of Internet of Things (IoT) devices across domains such as smart homes, industrial control systems, and healthcare networks has significantly expanded the attack surface for cyber threats, including botnet-driven distributed denial-of-service (DDoS), malware injection, and data exfiltration. Conventional intrusion detection systems (IDS) face critical challenges like privacy, scalability, and robustness when applied in such heterogeneous IoT environments. To address these issues, we propose SecureDyn-FL, a comprehensive and robust privacy-preserving federated learning (FL) framework tailored for intrusion detection in IoT networks. SecureDyn-FL is designed to simultaneously address multiple security dimensions in FL-based IDS: (1) poisoning detection through dynamic temporal gradient auditing, (2) privacy protection against inference and eavesdropping attacks through secure aggregation, and (3) adaptation to heterogeneous non-independent-and-identically-distributed (non-IID) data via personalized learning. The framework introduces three core contributions: (i) a dynamic temporal gradient auditing mechanism that leverages Gaussian mixture models (GMMs) and Mahalanobis distance (MD) to detect stealthy and adaptive poisoning attacks, (ii) an optimized privacy-preserving aggregation scheme based on transformed additive ElGamal encryption with adaptive pruning and quantization for secure and efficient communication, and (iii) a dual-objective personalized learning strategy that improves model adaptation under non-IID data using logit-adjusted loss. Extensive experiments on the N-BaIoT dataset under both IID and non-IID settings, including scenarios with up to 50% adversarial clients, demonstrate that SecureDyn-FL consistently outperforms state-of-the-art FL-based IDS defenses. It achieves up to 99.01% detection accuracy, a 98.9% F1-score, and significantly reduced attack success rates across diverse poisoning attacks, while maintaining strong privacy guarantees and computational efficiency for resource-constrained IoT devices.	10.1109/TNSM.2025.3647642
Lei Zhang, Wanting Su, Qin Ni, Jiawangnan Lu, Bin Chen	GAN4RM: A CWGAN-Based Framework for Radio Maps Generation in Real Cellular Networks	2026	Vol. 23, Issue	Generative adversarial networks Data models Wireless communication	With the evolution of mobile networks towards Artificial Intelligence as a Service (AIaaS), generative radio maps not only need to reflect the signal strength distribution in specific areas, but also possess the capability of proactive prediction. However, due to the rapid updates in urban infrastructure and the network iterations, crafting radio maps in complex urban environments represents a substantial challenge. In this paper, a multi-output framework for generating radio maps in real multi-building scenarios is proposed, based on Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) extracted from actual urban and suburban Measurement Reports (MRs). Specifically, An image encoding method integrating environmental features and base station system information is designed, while considering the sector antenna characteristics in actual communication environments. Then, a multi-output Conditional Wasserstein Generative Adversarial Network (CWGAN) is constructed for image conversion, and the radio maps are generated by learning the mapping from environmental & system information to RSRP & RSRQ radio maps, on the basis of image encoding that incorporates the physical laws of radio propagation. By calculating the priority of communication link gains at receiving points, it provides generative networks with reliable theoretical basis and conditional information, for serving cells and first neighboring cells. Experimental results show that the root mean square errors (RMSE) of the proposed method for RSRP / RSRQ of serving and neighboring cells are 1.7821 / 2.2251 and 0.8108 / 1.5121, which demonstrates the proposed method outperforms the baseline results. Simultaneously radio maps generation endows the cellular network with a certain “prophetic” capability, significantly enhancing the live service experience.	10.1109/TNSM.2025.3645305
Jiahe Xu, Jing Fu, Bige Yang, Zengfu Wang, Jingjin Wu, Xinyu Wang, Moshe Zukerman	Network Slicing in MEC-Based RANs With Nonlinear Cost Rate Functions	2026	Vol. 23, Issue	Resource management Costs Network slicing	This paper addresses network slicing in a large-scale Multi-Access Edge Computing (MEC)-enabled Radio Access Network (RAN) comprising heterogeneous edge nodes with varying computing and storage resource capacities. These resources are dynamically allocated to slice requests and released when the service of a slice request is completed. Our objective is to optimize the resource allocation for each admitted arriving slice request, considering its demands for computing and storage resources, to maximize the long-run average Earning Before Interest and Taxes (EBIT) of the MEC slicing system. We formulate the optimization problem as a Restless Multi-Armed Bandit (RMAB)-based resource allocation problem with a nonlinear cost rate function. To solve this, we introduce a new policy called Prioritizing-the-Future-Approximated earning per request (PFA) where for each admitted slice request, we always prioritize the allocation of the resource combination that gives the highest achievable earning, considering the future effects of this allocation. PFA is designed to be scalable and applicable to large-scale networks. We numerically demonstrate the superior performance of PFA in maximizing long-run average EBIT through simulations, comparing it with two baseline policies, at various cases of parameter values. Moreover, our findings offer insights for network operators in resource allocation policy selection.	10.1109/TNSM.2025.3646478