TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Zening Li, Pin-Han Ho	Topology-Constrained Generative Modeling for Performance Monitoring and Structural Anomaly Definition in Optical Transport Networks	2026	Early Access	Windows Modeling Semantics	Electrical-layer performance monitoring (PM) in optical transport networks is reported as window-aggregated counters, under which propagation delay and signal regeneration boundaries are not directly observable. Consequently, different impairment mechanisms can lead to statistically similar PM trajectories, leaving PM-only methods without a principled notion of nominal behavior or anomaly detectability. To address this limitation, this paper proposes Topology-Constrained Partially Observed Dynamical System (TC-PODS). TC-PODS defines a topology-consistent nominal predictive reference directly in PM space by introducing a latent impairment state (LAT) that enforces service-path propagation and regeneration semantics as supervisory constraints, while explicitly modeling the irreversible projection induced by windowed PM observation. This formulation disambiguates nominal variability from anomalous behavior under partial observability and defines anomalies as PM trajectories that are incompatible with any topology-consistent nominal generation. By shifting PM analysis from algorithm-centric detection toward detectability-aware inference under the deployed observation protocol, TC-PODS provides a principled framework for forecasting and anomaly interpretation under partial observability, with future applications to structure-aware failure management.	10.1109/TNSM.2026.3715222
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
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
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
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
Xiaolan Ji, Biao Han, Yuedong Xu, Jinshu Su	ICCP: Towards Congestion Control Agent via Controlling Logic Decoupling and Algorithm Integration	2026	Early Access	Algorithms Fluid flow Modeling	To address the limitations of single congestion control algorithms (CCAs) in dynamic and heterogeneous network environments, selecting an appropriate algorithm from a pool of existing ones has become a widely adopted strategy. Existing mechanisms, however, are typically constrained by the Linux kernel’s unified abstractions, which limit the flexibility of selecting from a small set of in-kernel CCAs. Learning-based CCAs further increase the deployment cost because their inference logic is often compute-intensive and can block concurrent flows when executed within a synchronous control path. In this paper, we present ICCP, a unified congestion control framework that supports both heuristic and compute-intensive algorithms for concurrent flows. Rather than introducing a new reinforcement learning method, ICCP provides a three-layer, decoupled runtime framework consisting of the protocol stack, the user-space algorithm library, and the congestion control agent. ICCP uses asynchronous request handling, a shared proxy, and a “zero-copy” serialization-based RPC path to support both batch and single-inference modes with controlled communication overhead. We implement three distinct reinforcement learning-based congestion control algorithms within ICCP, including Sage, Orca, and DTCC, to evaluate the framework using representative compute-intensive CCAs. Simulations and real-world experiments demonstrate that ICCP maintains robust and efficient communication and inference performance as the number of concurrent flows increases. Overall, ICCP provides a practical runtime framework for integrating, evaluating, and deploying heterogeneous congestion control algorithms in multi-flow environments.	10.1109/TNSM.2026.3714992
Enrico Boffetti, Arcangela Rago, Giuseppe Piro, Gennaro Boggia	Predictive QoE-Driven Radio Resource Management via Network Digital Twin in 5G and Beyond Networks	2026	Early Access		The evolution toward Beyond 5G networks introduces stringent requirements for intelligent Radio Resource Management (RRM) capable of jointly optimizing Quality of Experience (QoE) and resource utilization under highly dynamic conditions. This paper proposes a predictive QoE-driven RRM framework built upon an AI-enabled Network Digital Twin (NDT), which operates as a high-fidelity replica of the physical network to support proactive and efficient system-level resource allocation. The proposed approach integrates a Deep Learning (DL)-based module for forecasting future objective QoE metrics, namely Mean Opinion Score (MOS) values, with a Deep Reinforcement Learning (DRL) agent for dynamic Physical Resource Block (PRB) allocation. By incorporating predicted QoE levels over a finite horizon into the DRL agent’s state representation, the framework enables foresighted and policy-aware resource management while reducing synchronization overhead between the NDT and the physical infrastructure. Extensive simulations under heterogeneous traffic loads and QoE policies demonstrate that the proposed approach maintains high median QoE levels while adaptively regulating resource utilization, avoiding the systematic saturation observed with baseline static schedulers. The results further highlight stable learning behavior across DRL variants and confirm real-time feasibility with limited computational overhead.	10.1109/TNSM.2026.3716878
Sanku Kumar Roy, Mohamed Samshad, Ketan Rajawat	UNet: A Generic and Reliable Multi-UAV Communication and Networking System Architecture for Heterogeneous Applications	2026	Early Access	Autonomous aerial vehicles Architecture Computer architecture	The rapid growth of UAV applications necessitates a robust communication and networking system architecture capable of addressing the diverse requirements of various applications concurrently, rather than relying on applicationspecific solutions. This paper proposes a generic and reliable multi-UAV communication and networking system architecture designed to support the varying demands of heterogeneous applications, including short-range and long-range communication, star and mesh topologies, different data rates, and multiple wireless standards. Our architecture is designed for both ad hoc and infrastructure networks, ensuring seamless connectivity throughout the network. Additionally, we present the design of a multi-protocol UAV gateway that enables interoperability among various communication protocols to enhance connectivity. Furthermore, we introduce a data processing and service layer framework with a graphical user interface of a ground control station that facilitates remote control and monitoring from any location at any time. We practically implemented the proposed architecture and evaluated its performance using different metrics, demonstrating its effectiveness.	10.1109/TNSM.2026.3715386
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
Abdullatif Albaseer, Elmahdi Bentafat, Mohamed Abdallah, Saif Al-Kuwari, Marwa Qaraqe	Incentive-Driven Honeypot Defense: A Multi-Agent DRL Framework for Securing Smart Grid Networks	2026	Early Access	Pricing Timing Learning (artificial intelligence)	Honeypot defenses present a robust solution for securing the Advanced Metering Infrastructure (AMI) against sophisticated cyberattacks. The efficacy of AMI defenses relies on the strategic deployment of honeypots by Small-scale Power Suppliers (SPSs) and the subsequent exchange of defense data with Traditional Power Retailers (TPRs). However, existing methods are limited by their requirement for prior information exchange and their inability to specify targeted services (i.e., protocols), making them impractical for dynamic environments. In addition, previous approaches have predominantly overlooked critical aspects such as service allocation and competition among SPSs. To address these challenges, we propose a novel Deep Reinforcement Learning (DRL)-based Stackelberg leader-follower game framework that facilitates tailored service allocation and fosters competition among SPSs. We leverage the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which combines centralized training and distributed execution, to enable each SPS to autonomously learn optimal strategies that consider defense data quality and attack rates, without requiring prior knowledge of deployment costs or the actions of other SPSs. This adaptability minimizes the overhead associated with information exchange and improves the defense of vulnerable services. In particular, our method operates by requiring data collection, as each SPS functions as an independent learning agent that generates its own training experiences. Extensive simulations demonstrate that our proposed DRL-driven approach significantly outperforms baseline methods in different performance metrics. It achieves higher utility for both SPSs and TPR while maintaining lower operational costs.	10.1109/TNSM.2026.3715400
Antonio Iacobelli, Giorgio Franceschelli, Lorenzo Rinieri, Mirco Musolesi, Marco Prandini, Franco Callegati	SIP-Classifier: Unsupervised Classification of SIP-IMS Signaling with Transformer and Clustering	2026	Early Access	Modeling Sequences Sequential analysis	Ensuring the reliability of voice services in 5G networks requires effective detection of anomalies in IMS signaling. However, this task remains challenging due to the architectural complexity of IMS and the large volume of signaling data. In this paper, we propose SIP-Classifier, an unsupervised methodology that combines Transformer-based representation learning with clustering to identify anomalous SIP sequences. The approach encodes SIP messages through protocol-aware tokenization, learns latent representations via an autoregressive Transformer, and clusters them to distinguish valid from anomalous flows. We evaluate the method on real-world IMS data collected from operational 5G networks. It achieves 98% accuracy, 98% precision, 95% recall, and a 96% F1-score, significantly outperforming state-of-the-art approaches.	10.1109/TNSM.2026.3715301
Kaifei Peng, Yanbiao Li, Wenbin Li, Yuxuan Chen, Xian Yu, Xin Wang, Bo Pang, Gaogang Xie	Rethinking Virtual Network Construction for Network Emulation at Scale: Analysis, Modeling, and Optimization	2026	Early Access	Virtual machines Memory Timing	Network emulation has become an indispensable methodology for evaluating next-generation network architectures, offering a critical balance between experimental fidelity and operational scalability. However, its effectiveness is fundamentally constrained by inefficiencies in emulating large-scale networks, particularly during virtual network construction. This bottleneck arises from mandatory serialization of virtual link instantiation and operating system (OS) kernel-level notification overheads, which collectively degrade performance by orders of magnitude on 10K-node topologies. Departure from the current practice that employs a multi-machine framework for improvements, we propose SplitNN (Split Network and Namespace), a novel single-machine network emulation paradigm that breaks the serialization constraint through multi-VM (virtual machines) partitioning, and reduces notification overheads via namespace segmentation. Extensive evaluations show that SplitNN constructs 10K-node virtual networks within 1–5 minutes on a single machine, achieving a 98.5%–99.2% reduction in construction time compared to state-of-the-art emulators. While primarily a single-machine solution, SplitNN seamlessly integrates with multi-machine deployments, complementing them by enabling cumulative gains in both scalability and efficiency.	10.1109/TNSM.2026.3715559
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
Shiqi Fan, Zebo Huang, Furong Lin, Huan Luo, Yingya Guo	A Robust Traffic Engineering Method with Siamese GCN-Based Link Failure Awareness	2026	Early Access	Modeling Joining processes Routing	Traffic Engineering plays a critical role in optimizing network performance, improving resource utilization, and reducing congestion. However, existing TE methods generally provide limited capability to model topology changes caused by link failures, leading to performance degradation when link failures occur. To address this issue, we propose SGC-Net, a robust TE method based on a Siamese GCN with explicit failure-awareness. To effectively capture the topology changes between the normal and failure topologies, we employ a Siamese GCN with a shared architecture in the proposed SGC-Net. This Siamese GCN encodes both normal and failure network topology, enabling the SGC-Net to capture the topology differences and extract failure-aware node representations. To enrich the training scenarios and enhance the model’s generalization ability to unexpected link failures, we propose a hybrid training mechanism that injects random link failures into the network topology, enabling the model to learn from both normal and failure topologies simultaneously. Extensive experiments on four real-world network topologies demonstrate that SGC-Net achieves near-optimal load balancing performance under normal networks, while exhibiting strong routing robustness across diverse link failure scenarios.	10.1109/TNSM.2026.3716021
Mengdi Liu, Mingwei Lin, Shenbao Yu, Riqing Chen, Xin Luo	A Lightweight and Robust Low-rank Tensor Embedding-integrated VAE for Network Anomaly Detection	2026	Early Access	Modeling Tensors Anomaly detection	As networks scale exponentially, robust anomaly detection has become critical. Although numerous detection algorithms have been proposed, most rely on linear matrix-based models that fail to effectively capture multidimensional structural characteristics (including temporal patterns, node locations, and more) or the underlying nonlinear relationships. Moreover, periodic temporal patterns are often overlooked, degrading detection performance. To address these limitations, we propose a novel Tensor-based Embedding Variational Autoencoder (TEVAE) model. TEVAE introduces a tensor representation framework that integrates deep temporal feature extraction with probabilistic reconstruction. It employs bidirectional long short-term memory networks to distill temporally enriched latent representations from contaminated network tensors. These representations are processed through a probabilistic variational framework, enabling robust learning of latent embeddings and high-fidelity reconstruction of denoised outputs. To further reduce computational demand, we propose LightTEVAE. It decomposes the network tensor via dimension preserved tensor decomposition into low-rank factors, which are embedded as compact representations for encoding, thereby reducing complexity while preserving structural integrity. Experimental results on public network datasets demonstrate that: 1) TEVAE achieves superior anomaly detection performance, consistently outperforming nine main-stream methods by a clear margin; 2) LightTEVAE effectively maintains this high detection performance while substantially reducing computational overhead. These results demonstrate the superiority of both proposed models in temporal feature extraction, complex pattern learning, and large-scale processing efficiency for network anomaly detection.	10.1109/TNSM.2026.3716561
Madhura Adeppady, Yenchia Yu, Ali Rahmanian, Ahmed Ali-Eldin Hassan, Carla Fabiana Chiasserini	Efficient Management of Composite Heterogeneous Applications at the Network Edge	2026	Early Access	Central Processing Unit Servers Resource management	Edge computing is a promising paradigm for deploying latency-sensitive applications (Apps) as it brings resources closer to end users. Edge Apps often adopt a microservice (MS) architecture, breaking monolithic Apps into lightweight, containerized MSs that can be dynamically and independently deployed. However, managing such Apps involves three key challenges: (i) optimizing the placement of MSs to reduce both response time and resource overhead, (ii) handling MS migration or relocation as users move while minimizing App service disruption (App downtime), and (iii) enabling MS sharing across Apps while ensuring performance guarantees. We formulate this as an optimization problem, named Multi-microservice Application Placement (MAP), prove its NP-hardness, and introduce STEP (State and Topology-aware Edge-MS Placement), a polynomial-time heuristic. STEP distinguishes itself from prior work by: (i) jointly considering stateful and stateless MS characteristics in deployment decisions, (ii) exploiting MS shareability to reduce resource usage, (iii) balancing response latency, App downtime, and resource utilization, and (iv) leveraging multiple versions of the same MS to adapt quality of service to available edge resources. Our results in a small-scale scenario show that STEP achieves near-optimal performance with only 7% higher CPU cost than the optimal solution. Large-scale real-time experiments on a Kubernetes cluster demonstrate that STEP consistently outperforms competing methods, achieving up to 50% lower deployment costs while delivering 50% gain in app quality and saving 15% in radio resources with over 90% request success rates.	10.1109/TNSM.2026.3709656
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
Dev Gurung, Shiva Raj Pokhrel	LLM-QFL: Distilling Large Language Model for Quantum Federated Learning	2026	Early Access	Modeling Federated learning Large language models	As Quantum Federated Learning (QFL) scales toward distributed quantum networks, managing heterogeneous resources and communication bottlenecks becomes a critical challenge. This research proposes LLM-QFL, an adaptive network service management framework that leverages Large Language Models (LLMs) to optimize the operational efficiency of QFL systems. We introduce a federated distillation method in which locally fine-tuned LLMs serve as autonomous network agents. These agents adaptively manage service parameters by: i) dynamically adjusting local computation intensity (optimizer steps) based on loss gradients, ii) performing variance-aware client selection to minimize network-wide heterogeneity, and iii) implementing intelligent early stopping criteria to conserve bandwidth. By serving as an orchestration layer, LLM-QFL provides a synergy between LLMs and quantum networking. Our contributions include: i) Adaptive Performance and Efficiency: Reducing idle computation and significantly cutting communication overhead; ii) Theoretical Rigor: Convergence guarantees of O(1/T) for the adaptive management protocol; and iii) Scalable Deployment: Implementing PEFT (LoRA/QLoRA) for resource-constrained quantum service nodes.	10.1109/TNSM.2026.3712394
Muhammad Ahsan, Thang X. Vu, Ilora Maity, Symeon Chatzinotas	VNF Mapping and Selective Handover for eMBB and mMTC Services in a LEO Satellite Network	2026	Vol. 23, Issue	Low earth orbit satellites Artificial satellites Aerospace and electronic systems	The integrated satellite-terrestrial networks (STNs) aim to provide global connectivity and support heterogeneous services, including enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC). Each service request requires a series of virtual network functions (VNFs) to be deployed consecutively. The VNFs are mapped on nodes that constitute a path where a request is mapped. Provisioning multiple slices through satellite networks is challenging due to limited storage and computation resources. In addition, there are dynamic changes in the satellite positions that cause frequent variations in the topology. For requests lasting more than one time frame, a handover can be performed at the beginning of the next time frame. Handover implies overall reconfiguration, which induces significant computation costs in satellite networks. Therefore, in this article, we propose a path and VNF mapping strategy with selective handover while considering dynamic changes in the satellite topology and the limitation of available resources. We formulate a mathematical model based on Binary Integer Linear Programming (BILP), aiming to maximize the served requests. To reduce the time complexity of the model, we solve it using an iterative algorithm based on successive convex approximation (SCA). We refine the solution of the SCA after binary recovery using VNF and path mapping algorithm. For the mapped multi-frame requests in the current time frame, the resources are reserved in the nodes and links for the next time frame if the current routing path is available for the remaining duration of the request. The simulation results certify the performance of the proposed technique with a significant improvement in the served request percentage compared to previous works in the literature, while also reducing the number of handovers.	10.1109/TNSM.2026.3687390
Faissal Ahmadou, Boubakr Nour, Makan Pourzandi, Mourad Debbabi, Chadi Assi	Automating Threat-Aligned Testflows Generation Using Ontology-Grounded RAG From CTI Reports	2026	Vol. 23, Issue	Radio broadcasting Frequency modulation System-on-chip	The increasing sophistication and complexity of Advanced Persistent Threats (APTs) pose significant challenges to security practitioners. To proactively protect against these threats, security practitioners rely on the generation of testflows, structured sequences of actions designed to verify whether the tactics and behaviors of an APT are present within their organization. However, manually creating such testflows is time-consuming, error-prone, and highly dependent on expert knowledge. Moreover, existing automated approaches suffer from several limitations, including validity, efficiency, and insufficient domain adaptation. To address these challenges, this paper introduces CTI-RAGFlow, to automate the generation of relevant, valid, and effective testflows from unstructured threat reports tailored to specific organizational environments. CTI-RAGFlow introduces three key contributions: (i) a dual-ontology approach, that integrates both a system ontology representing the operational environment and a cybersecurity ontology capturing adversary tactics, techniques, and procedures, improving the precision and accuracy of generated testflows; (ii) a fact-based context retrieval mechanism that combines a hypergraph structured knowledge base with a Retrieval-Augmented Generation pipeline using Large Language Models; and (iii) a fully automated testflow generation process that minimizes manual effort, reduces human error, and facilitates the generation of valid testflow. We evaluate CTI-RAGFlow against three widely used LLM models (e.g., base and fine-tuned models) using publicly available CTI reports for three well-known APTs (e.g., APT41, APT29, APT28). The results show that CTI-RAGFlow outperforms the baselines in terms of semantic relevance, coverage, validity, and effectiveness in verifying multi-stage cyberattack scenarios.	10.1109/TNSM.2026.3684808