TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Junfeng Tian, Rongyi Fei	A certificateless dynamic anonymous authentication scheme for mobile edge computing	2026	Early Access	Authentication Servers Security	In mobile edge computing (MEC), many identity-based authentication schemes rely on an unrealistic prior-knowledge assumption about edge server identities, which limits their applicability in highly dynamic MEC environments. On the other hand, although certificateless schemes alleviate the computational overhead introduced by bilinear pairings in ID-based schemes, they face new challenges regarding secure key storage and the achievement of full user anonymity. To simultaneously address these issues, this paper proposes a new certificateless dynamic anonymous authentication scheme based on physical unclonable functions (PUFs) and elliptic curve cryptography (ECC), tailored for authentication and key agreement between mobile users and edge servers in dynamic MEC environment. By leveraging PUFs, the scheme resolves the key storage issue commonly found in traditional certificateless authentication approaches. Additionally, the scheme supports dynamic anonymity and frequent updates of public–private key pairs, thereby enhancing system security and providing user with full anonymity and unlinkability. The proposed scheme is rigorously evaluated through both informal and formal security analyses, including BAN logic, the Real-Or-Random (ROR) model, and automated verification via ProVerif. Comparative results demonstrate that our scheme achieves stronger computational efficiency, lower energy consumption, lower average message delay, and acceptable communication and storage overhead, while maintaining robust security guarantees compared with recent state-of-the-art approaches in this field.	10.1109/TNSM.2026.3661192
Rajasekhar Dasari, Sanjeet Kumar Nayak	PR-Fog: An Efficient Task Priority-based Reliable Provisioning of Resources in Fog-Enabled IoT Networks	2026	Early Access	Reliability Internet of Things Costs	As the demand for real-time data processing grows, fog computing emerges as an alternative to cloud computing, which brings computation and storage closer to IoT devices. In Fog-enabled IoT networks, provisioning of fog nodes for task processing must consider factors, such as latency, energy consumption, cost, and reliability. This paper presents PR-Fog, a scheme for optimizing the provisioning of heterogeneous fog nodes in fog-enabled IoT networks, considering parameters such as task priority, energy efficiency, cost efficiency, and reliability. At first, we create an analytical framework using M/M/1/C priority queuing system to assess the reliability of these heterogeneous fog nodes. Building on this analysis, we propose an algorithm that determines the optimal number of reliable fog nodes while satisfying latency, energy, and cost constraints. Extensive simulations show significant enhancements in key performance metrics when comparing PR-Fog to existing schemes, including a 36% decrease in response time and an 8% improvement in satisfaction ratio, resulting in minimized 23% fog node provisioning costs. Additionally, PR-Fog’s effectiveness is validated through real testbed experiments.	10.1109/TNSM.2026.3661745
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
Abdinasir Hirsi, Mohammed A. Alhartomi, Lukman Audah, Mustafa Maad Hamdi, Adeb Salah, Godwin Okon Ansa, Salman Ahmed, Abdullahi Farah	Hybrid CNN-LSTM Model for DDoS Detection and Mitigation in Software-Defined Networks	2026	Early Access	Prevention and mitigation Denial-of-service attack Feature extraction	Software-Defined Networking (SDN) enhances programmability and control but remains highly vulnerable to distributed denial-of-service (DDoS) attacks. Existing solutions often adapt conventional methods without leveraging SDN’s native features or addressing real-time mitigation. This study introduces a novel hybrid deep learning framework for DDoS detection and mitigation in SDN, significantly advancing the state of the art. We develop a custom dataset in a Mininet–Ryu testbed that reflects realistic SDN traffic conditions, and employ a multistage feature selection pipeline to reduce redundancy and highlight the most discriminative flow attributes. A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model is then applied, capturing both spatial and temporal traffic patterns. The proposed system achieves 99.5% accuracy and a 97.7% F1-score, demonstrating a significant improvement over baseline ML and DL approaches. In addition, a lightweight and scalable mitigation module embedded in the SDN controller dynamically drops or reroutes malicious flows, enabling real-time, low-latency responsiveness. Experimental results across diverse topologies confirm the framework’s scalability and applicability in real-world SDN environments.	10.1109/TNSM.2026.3662819
Mohammad Amir Dastgheib, Hamzeh Beyranvand, Jawad A. Salehi	Shannon Entropy for Load-Balanced Cellular Network Planning: Data-Driven Voronoi Optimization of Base-Station Locations	2026	Early Access	Shape Entropy Costs	In this paper, we introduce a stochastic shape optimization technique for base-station placement in cellular wireless communication networks. We formulate the data-driven facility location problem in a gradient-based framework and propose an algorithm that computes stochastic gradients efficiently via nearest-neighbor evaluations on Voronoi diagrams. This enables the use of Shannon-entropy objectives that promote balanced coverage and yield more than two orders of magnitude reduction in per-iteration runtime compared to a conventional integral-based optimization that assumes full knowledge of the under-lying density, making the proposed approach practical for real deployments. We highlight the requirements of facility location balancing problems with the introduction of the Adjusted Entropy Ratio and show a significant improvement in load balancing compared to the baseline algorithms, particularly in scenarios where baseline algorithms fall short in subdividing crowded areas for more equitable coverage. A downlink telecom evaluation with realistic propagation and interference models further shows that the proposed method configuration substantially improves user-rate fairness and load balance. Our results also show that Self-Organizing Maps (SOMs) provide an effective initialization by capturing the structure of the users’ location data.	10.1109/TNSM.2026.3663045
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
Muhammad Fahimullah, Michel Kieffer, Sylvaine Kerboeuf, Shohreh Ahvar, Maria Trocan	Decentralized Coalition Formation of Infrastructure Providers for Resource Provisioning in Coverage Constrained Virtualized Mobile Networks	2026	Early Access	Indium phosphide III-V semiconductor materials Resource management	The concept of wireless virtualized networks enables Mobile Virtual Network Operators (MVNOs) to utilize resources made available by multiple Infrastructure Providers (InPs) to set up a service. Nevertheless, existing centralized resource provisioning approaches fail to address such a scenario due to conflicting objectives among InPs and their reluctance to share private information. This paper addresses the problem of resource provisioning from several InPs for services with geographic coverage constraints. When complete information is available, an Integer Linear Program (ILP) formulation is provided, along with a greedy solution. An alternative coalition formation approach is then proposed to build coalitions of InPs that satisfy the constraints imposed by an MVNO, while requiring only limited information sharing. The proposed solution adopts a hedonic game-theoretic approach to coalition formation. For each InP, the decision to join or leave a coalition is made in a decentralized manner, relying on the satisfaction of service requirements and on individual profit. Simulation results demonstrate the applicability and performance of the proposed solution.	10.1109/TNSM.2026.3663437
Fengqi Li, Yudong Li, Lingshuang Ma, Kaiyang Zhang, Yan Zhang, Chi Lin, Ning Tong	Integrated Cloud-Edge-SAGIN Framework for Multi-UAV Assisted Traffic Offloading Based On Hierarchical Federated Learning	2026	Early Access	Resource management Autonomous aerial vehicles Heuristic algorithms	The growing number of mobile devices used by terrestrial users has significantly amplified the traffic load on cellular networks. Especially in urban environments, the high traffic demand brought about by dense user populations has bottlenecked network resources. The Space-Air-Ground-Integrated Network (SAGIN) provides a new solution to cope with this demand, enhancing data transmission efficiency through a multi-layered network structure. However, the heterogeneous and dynamic nature of SAGIN also poses significant management and resource allocation challenges. In this paper, we propose a cloud-edge-SAGIN framework for multi-UAV assisted traffic offloading based on Hierarchical Federated Learning (HFL), aiming to improve the traffic offloading ratio while optimizing the offloading resource allocation. HFL is used instead of traditional Federated Learning (FL) to solve problems such as irrational resource allocation due to heterogeneity in SAGIN. Specifically, the framework applies a hierarchical federated average algorithm and sets a reward function at the ground level, aiming to obtain better model parameters, improve model accuracy at aggregation, enhance UAV traffic offloading ratio, and optimize its scheduling and resource allocation. In addition, an improved Reinforcement Learning (RL) algorithm TD3-A4C is designed in this paper to assist UAVs in realizing intelligent decision-making, reducing communication latency, and further improving resource utilization efficiency. Simulation results demonstrate that the proposed framework and algorithms display superior performance across all dimensions and offer robust support for the comprehensive investigation of intelligent traffic offloading networks.	10.1109/TNSM.2026.3658833
Xiaoshan Yu, Huaxi Gu, Qian Zhang	RCC: Rate-based Congestion Control for the Lossless Network	2026	Early Access	Flow production systems Switches Propagation losses	It has been widely accepted that hop-by-hop flow control is applied to High Performance Computing (HPC) interconnect network to ensure lossless transmission. However, hop-by-hop flow control directly interferes with existing congestion control because of inaccurate congestion detection. The aim of this study is to eliminate the interference of hop-by-hop flow control on congestion detection and the interference of flow rate changes on rate adjustment in lossless networks. We designed Rate-based Congestion Control (RCC), which includes a new congestion detection mechanism based on the source sending rate. Combined with congestion detection, we designed an individual rate control mechanism that slows down congested flows and accelerating victim flows. The extensive simulation results based on general traffic patterns and benchmark for HPC systems show that compared with the existing congestion control strategies of the lossless networks, RCC improves 99th percentile FCT performance by 12.55∼29.63%, and the maximum reduction in congestion impact reaches 40.34%.	10.1109/TNSM.2026.3661289
Awaneesh Kumar Yadav, An Braeken, Madhusanka Liyanage	A Provably Secure Lightweight Three-factor 5G-AKA Authentication Protocol relying on an Extendable Output Function	2026	Early Access	Authentication Protocols Security	Compared to 4G, the designed authentication and key agreement protocol for 5G communication (5G-AKA) offers better security. State-of-the-art shows that various protocols indicate the flaws in the 5G-AKA and suggest solutions primarily for the desynchronization attack, traceability attack, and perfect forward secrecy. However, most authentication protocols fail to facilitate the device stolen attack and are expensive; they also do not consider the prominent security issues such as post-compromise security and non-repudiation. Considering the above demerits of these protocols and the necessity to offer additional security, a provably secure lightweight 5G-AKA multi-factor authentication protocol relying on an extendable output function is proposed. The security of the proposed work has been confirmed informally and formally (ROR logic, GNY logic, and Scyther tool) to ensure that the proposed work handles all types of attacks and offers additional security features, such as post-compromise features and non-repudiation. Furthermore, we compute the performance of the proposed work and compare it with its counterparts to show that our work is less costly and more suitable for lightweight devices than others in terms of computational, communication, storage, and energy consumption cost.	10.1109/TNSM.2026.3656167
Zhiwei Yu, Chengze Du, Heng Xu, Ying Zhou, Bo Liu, Jialong Li	REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks	2026	Early Access	Graphics processing units Computational modeling Reliability	Community GPU(Graphics Processing Unit) platforms are emerging as a cost-effective and democratized alternative to centralized GPU clusters for AI(Artificial Intelligence) workloads, aggregating idle consumer GPUs from globally distributed and heterogeneous environments. However, their extreme hardware/software diversity, volatile availability, and variable network conditions render traditional schedulers ineffective, leading to suboptimal task completion. In this work, we present REACH (Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks), a Transformer-based reinforcement learning framework that redefines task scheduling as a sequence scoring problem to balance performance, reliability, cost, and network efficiency. By modeling both global GPU states and task requirements, REACH learns to adaptively co-locate computation with data, prioritize critical jobs, and mitigate the impact of unreliable resources. Extensive simulation results show that REACH improves task completion rates by up to 17%, more than doubles the success rate for high-priority tasks, and reduces bandwidth penalties by over 80% compared to state-of-the-art baselines. Stress tests further demonstrate its robustness to GPU churn and network congestion, while scalability experiments confirm its effectiveness in large-scale, high-contention scenarios.	10.1109/TNSM.2026.3663316
Liang Kou, Xiaochen Pan, Guozhong Dong, Meiyu Wang, Chunyu Miao, Jilin Zhang, Pingxia Duan	Dynamic Adaptive Aggregation and Feature Pyramid Network Enhanced GraphSAGE for Advanced Persistent Threat Detection in Next-Generation Communication Networks	2026	Early Access	Feature extraction Adaptation models Computational modeling	Advanced Persistent Threats (APTs) pose severe challenges to Next-Generation Communication Networks (NGCNs) due to their stealthiness and NGCNs’ dynamic topology, while conventional GNN-based intrusion detection systems suffer from static aggregation and poor adaptability to unseen nodes. To address these issues, this paper proposes DAA-FPN-SAGE, a lightweight graph-based detection framework integrating Dynamic Adaptive Aggregation (DAA) and Multi-Scale Feature Pyramid Network (MSFPM). Leveraging GraphSAGE’s inductive learning capability, the framework effectively models unseen nodes or subgraphs and adapts to NGCN’s dynamic changes (e.g., elastic network slicing, online AI model updates)—a key advantage for handling NGCN’s real-time topological variations. The DAA module employs multi-hop attention to dynamically assign weights to neighbors at different hop distances, enhancing capture of hierarchical dependencies in multi-stage APT attack chains. The MSFPM module fuses local-global structural information via a gated feature selection mechanism, resolving dimensional inconsistency and enriching attack behavior representation. Extensive experiments on StreamSpot, Unicorn, and DARPA TC#3 datasets demonstrate superior performance, meeting detection requirements of large-scale NGCNs.	10.1109/TNSM.2026.3660650
Yuchao Dang, Xuefen Chi, Linlin Zhao, Zhu Han	Improving Spectrum Efficiency through Multi-hop QoS Analysis and Interference Decomposition in Integrated Access and Backhaul Networks	2026	Early Access	Interference Quality of service Backhaul networks	The dense deployment of Integrated Access and Backhaul (IAB) networks exacerbates spectrum consumption. This paper aims to enhance Spectrum Efficiency (SE) in IAB networks through multi-hop Quality of Service (QoS) analysis and network interference decomposition. We propose a multi-hop delay QoS analysis method that increases computational efficiency and accuracy, thus preventing spectrum over-allocation. We introduce a Transformer-based Interference Path Loss Assessment Neural Network (TIPA-NN) to tackle the issue of inadequate interference information in complex IAB networks, ensuring efficient spectrum reuse. The simulation results show that the proposed QoS analysis method effectively approximates delay unreliability probability across varying hop counts, demonstrating good scalability. The minimum service rate derived supports diverse QoS requirements in multi-hop scenarios. Our algorithm guarantees QoS and enhances SE in IAB networks, outperforming baselines and exhibiting topology-agnostic adaptability. Notably, there is a minimum of 25.03% reduction in subcarrier consumption compared to existing approaches, while ensuring improved SE.	10.1109/TNSM.2026.3660735
Xiujun Xu, Qi Wang, Qingshan Wang, Yinlong Xu	Contract-Based Incentive Mechanism for Long-term Participation in Federated Learning	2026	Early Access	Contracts Data models Computational modeling	Federated learning (FL), as a newly-developing technique, brings the advantage of organizing multiple participants to learn together, while avoiding the leakage of their privacy information. Contract theory provides an effective incentive mechanism to encourage participants to participate in FL. Existing contract-based incentive mechanisms consider participants’ types but ignore the different contributions of participants within the same type during the training.This paper first introduces a metric, reputation, to evaluate the contribution of participants in each iteration, and then proposes a hybrid contract mechanism consisting of a short-term contract and a long-term contract. Only the participants with reputations higher than a pre-defined threshold can sign the long-term contract. We formulate the solution of the long-term contract mechanism as an optimization problem with constraints. We further simplify the constraints of the long-term contract optimization problem, and theoretically analyze the correctness of the simplification to greatly reduce its computational complexity. We prove that the model owner achieves more profit with the hybrid contract mechanism. Simulations with the MNIST dataset show that the long-term contract improves the model accuracy by at least 5% compared with the existing contracts. Furthermore, compared with the short-term contract, participants signing the long-term contract are granted more rewards.	10.1109/TNSM.2026.3657419
Xinshuo Wang, Lei Liu, Baihua Chen, Yifei Li	ENCC: Explicit Notification Congestion Control in RDMA	2026	Early Access	Bandwidth Data centers Heuristic algorithms	Congestion control (CC) is essential for achieving ultra-low latency, high bandwidth, and network stability in high-speed networks. However, modern high-performance RDMA networks, crucial for distributed applications, face significant performance degradation due to limitations of existing CC schemes. Most conventional approaches rely on congestion notification signals that must traverse the queuing data path before congestion signals can be sent back to the sender, causing delayed responses and severe performance collapse. This study proposes Explicit Notification Congestion Control (ENCC), a novel high-speed CC mechanism that achieves low latency, high throughput, and strong network stability. ENCC employs switches to directly notify the sender of precise link load information and avoid notification signal queuing. This allows precise sender-side rate control and queue regulation. ENCC also ensures fairness and easy deployment in hardware. We implement ENCC based on FPGA network interface cards and programmable switches. Evaluation results show that ENCC achieves substantial through-put improvements over representative baseline algorithms, with gains of up to 16.6× in representative scenarios, while incurring minimal additional latency.	10.1109/TNSM.2026.3656015
Muhammad Umar Farooq Qaisar, Weijie Yuan, Lin Zhang, Shehzad Ashraf Chaudhry, Guangjie Han, Yunyang Zhang	A Robust Trust Management System for V2X Networks Integrating ISAC with Blockchain Smart Contracts	2026	Early Access		Vehicle-to-everything (V2X) networks face critical security challenges due to their dynamic nature, stringent latency requirements, and susceptibility to malicious attacks. Traditional trust management approaches often rely on centralized authorities or historical data, creating vulnerabilities and scalability limitations. This paper presents a new trust management system that leverages integrated sensing and communication (ISAC) technology and blockchain-based smart contracts to provide secure and decentralized trust evaluation in V2X networks. The proposed framework leverages real-time ISAC signal processing to compute five comprehensive trust metrics: behavior score, reputation score, safety score, uptime score, and response time score. These metrics are derived through advanced Kalman filtering and statistical anomaly detection applied to physical-layer measurements, enabling immediate detection of malicious activities that traditional approaches might miss. Trust records are securely stored and validated through smart contracts deployed on 5G base station blockchains, ensuring tamper-proof storage and automated policy enforcement. Numerical results demonstrate that the proposed protocol achieves faster trust convergence, higher communication reliability, significant reduction in false positive rates, improved detection accuracy, acceptable end-to-end latency, and lower computational overhead compared to state-of-the-art approaches.	10.1109/TNSM.2026.3658589
Hai Anh Tran, Nam-Thang Hoang	Towards Efficient and Adaptive Traffic Classification: A Knowledge Distillation-Based Personalized Federated Learning Framework	2026	Vol. 23, Issue	Adaptation models Training Federated learning	Traffic classification plays a crucial role in optimizing network management, enhancing security, and enabling intelligent resource allocation in distributed network systems. However, traditional Federated Learning (FL) approaches struggle with domain heterogeneity, as network traffic characteristics vary significantly across different domains due to diverse infrastructure, applications, and usage patterns. This results in degraded performance when applying a single global model across all domains. To overcome this challenge, we propose KD-PFL-TC, a Knowledge Distillation-based Personalized Federated Learning framework for Traffic Classification, aimed to balance global knowledge sharing with personalized model adaptation in heterogeneous network environments. Our approach leverages knowledge distillation to enable collaborative learning without directly sharing raw data, preserving privacy while mitigating the negative effects of domain shifts. Each domain refines its local model by integrating insights from a global model and peer domains while maintaining its unique traffic distribution. To further enhance performance, we introduce an adaptive distillation strategy that dynamically adjusts the influence of global, peer, and local knowledge based on the similarity between traffic distributions, ensuring optimal knowledge transfer designed to each domain’s characteristics. Extensive experiments on real-world traffic datasets show that KD–PFL–TC maintains 88.0% accuracy under high heterogeneity (vs. 75.0% for FedAvg) while reducing communication overhead by ~60%, delivering an efficient and robust solution for large-scale, heterogeneous networks.	10.1109/TNSM.2025.3629241
Agrippina Mwangi, León Navarro-Hilfiker, Lukasz Brewka, Mikkel Gryning, Elena Fumagalli, Madeleine Gibescu	A Threshold-Triggered Deep Q-Network-Based Framework for Self-Healing in Autonomic Software-Defined IIoT-Edge Networks	2026	Vol. 23, Issue	Switches Routing Quality of service	Stochastic disruptions such as flash events arising from benign traffic bursts and switch thermal fluctuations are major contributors to intermittent service degradation in software-defined industrial networks. These events violate IEC 61850-derived quality of service requirements and user-defined service-level agreements, hindering the reliable and timely delivery of control, monitoring, and best-effort traffic in IEC 61400-25-compliant wind power plants. Failure to maintain these requirements often results in delayed or lost control signals, reduced operational efficiency, and increased risk of wind turbine generator downtime. To address these challenges, this study proposes a threshold-triggered Deep Q-Network self-healing agent that autonomically detects, analyzes, and mitigates network disruptions while adapting routing behavior and resource allocation in real time. The proposed agent was trained, validated, and tested on an emulated tri-clustered switch network deployed in a cloud-based proof-of-concept testbed. Simulation results show that the proposed agent improves disruption recovery performance by 53.84% compared to a baseline shortest-path and load-balanced routing approach, and outperforms state-of-the-art methods, including the Adaptive Network-based Fuzzy Inference System by 13.1% and the Deep Q-Network and Traffic Prediction-based Routing Optimization method by 21.5%, in a super-spine leaf data-plane architecture. Additionally, the agent maintains switch thermal stability by proactively initiating external rack cooling when required. These findings highlight the potential of deep reinforcement learning in building resilience in software-defined industrial networks deployed in mission-critical, time-sensitive application scenarios.	10.1109/TNSM.2025.3647853
Shaocong Feng, Baojiang Cui, Junsong Fu, Meiyi Jiang, Shengjia Chang	Adaptive Target Device Model Identification Attack in 5G Mobile Network	2026	Vol. 23, Issue	Object recognition Adaptation models 5G mobile communication	Enhanced system capacity is one of 5G goals. This will lead to massive heterogeneous devices in mobile networks. Mobile devices that lack basic security capability have chipset, operating system or software vulnerability. Attackers can perform Advanced Persistent Threat (APT) Attack for specific device models. In this paper, we propose an Adaptive Target Device Model Identification Attack (ATDMIA) that provides the prior knowledge for exploiting baseband vulnerability to perform targeted attacks. We discovered Globally Unique Temporary Identity (GUTI) Reuse in Evolved Packet Switching Fallback (EPSFB) and Leakage of User Equipment (UE) Capability vulnerability. Utilizing silent calls, an attacker can capture and correlate the signaling traces of the target subscriber from air interface within a specific geographic area. In addition, we design an adaptive identification algorithm which utilizes both invisible and explicit features of UE capability information to efficiently identify device models. We conducted an empirical study using 105 commercial devices, including network configuration, attack efficiency, time overhead and open-world evaluation experiments. The experimental results showed that ATDMIA can accurately correlate the EPSFB signaling traces of target victim and effectively identify the device model or manufacturer.	10.1109/TNSM.2025.3626804
Abdurrahman Elmaghbub, Bechir Hamdaoui	HEEDFUL: Leveraging Sequential Transfer Learning for Robust WiFi Device Fingerprinting Amid Hardware Warm-Up Effects	2026	Vol. 23, Issue	Fingerprint recognition Radio frequency Hardware	Deep Learning-based RF fingerprinting approaches struggle to perform well in cross-domain scenarios, particularly during hardware warm-up. This often-overlooked vulnerability has been jeopardizing their reliability and their adoption in practical settings. To address this critical gap, in this work, we first dive deep into the anatomy of RF fingerprints, revealing insights into the temporal fingerprinting variations during and post hardware stabilization. Introducing HEEDFUL, a novel framework harnessing sequential transfer learning and targeted impairment estimation, we then address these challenges with remarkable consistency, eliminating blind spots even during challenging warm-up phases. Our evaluation showcases HEEDFUL‘s efficacy, achieving remarkable classification accuracies of up to 96% during the initial device operation intervals—far surpassing traditional models. Furthermore, cross-day and cross-protocol assessments confirm HEEDFUL’s superiority, achieving and maintaining high accuracy during both the stable and initial warm-up phases when tested on WiFi signals. Additionally, we release WiFi type B and N RF fingerprint datasets that, for the first time, incorporate both the time-domain representation and real hardware impairments of the frames. This underscores the importance of leveraging hardware impairment data, enabling a deeper understanding of fingerprints and facilitating the development of more robust RF fingerprinting solutions.	10.1109/TNSM.2025.3624126