TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Yakai Dai, Jia Chen, Xu Huang, Rui Dong, Tao Fu	VISTA: Verified Belief-Centric LLM Agents for Safe Network Management	2026	Early Access	Safety Writing Modeling	Large language model (LLM) agents now commit changes in production network management, where one change can touch routing-protocol state or a Kubernetes NetworkPolicy. The prevailing pipeline emits the change as a single text answer, so the network state, the competing root-cause hypotheses, and the operational invariants travel through the same token stream as the answer—a design that is unsafe on the NetArena dynamic benchmark: on capacity planning, the strongest supervised and retrieval-augmented baselines reach near-perfect correctness while leaving 41% of executed actions unsafe. VISTA gives each hidden quantity a persistent owner: it maintains a typed belief graph over topology and configuration, reasons over an explicit hypothesis trace, and routes every candidate write through a counterfactual verifier that emits a typed proof object, whose structured failure reasons re-enter the next round. On a shared Qwen2.5-Coder-7B backbone and NetArena’s official scorer, no single prompting strategy is safe across capacity planning’s ten query classes. Under π=WITHHOLD (proof failure blocks the write), VISTA reaches safety 1.000 at commit 0.869. Under π=FLAG (proof failure annotates but proceeds), it attains the per-class maximum on all ten classes, lifting safety from the 0.709 per-class oracle envelope to 0.869 at correctness 0.992, commit 1.000, and zero refusals, with a paired query-level margin of 153–0. An ablation attributes the gain: the gate itself does not move the safety rate, which is set by feeding belief and hypothesis structure back into generation, while the gate contributes a commit-time certificate agreeing with the official scorer on 869/869 admitted writes and a rejection whose structure decides repairability (60/86 versus 0/86), at ≈13× latency (3.520 s). On routing and Kubernetes the same workflow trades ≈70% refusal for high safety and is diagnostic, not deployable. Our implementation is released at https://github.com/dai-yakai/VISTA.	10.1109/TNSM.2026.3745152
Martha Cash, Charlotte Fowler, Alexander M. Wyglinski	On the Role of Time Series Clustering in Traffic Matrix Prediction	2026	Early Access	Fluid flow Timing Modeling	This paper analyzes the role of time-series clustering in traffic matrix (TM) prediction. Traffic flows within a TM often exhibit heterogeneous behavior, which can reduce the effectiveness of global forecasting models that predict all flows jointly. To address this, we propose a clustering-based prediction framework that groups flows into smaller subsets and trains separate predictors for each group. We evaluate four traffic-flow representations for clustering, namely histogram, autocorrelation function (ACF), power spectral density (PSD), and naïve partitioning, and analyze how both the representation choice and the number of clusters affect prediction performance. Experiments using the publicly available Abilene and GÉANT datasets show that clustering consistently improves over global forecasting baselines, while remaining substantially less costly than local prediction. The results further show that most of the performance gain is achieved at moderate values of K, with diminishing returns as the number of clusters increases. Although different clustering representations produce different partitions of the traffic flows, they often achieve similar root mean squared error (RMSE). This suggests that the main benefit of clustering lies in decomposing the TM prediction task into smaller subproblems, while the exact cluster structure plays a more limited role in determining overall prediction accuracy.	10.1109/TNSM.2026.3747898
Mona Alghamdi, Atm S. Alam, Asma Cherif	A Context-Aware Action Filter for Deep Reinforcement Learning-Based Task Orchestration in Mobile Edge Computing	2026	Early Access	Filtering Servers Filters	The growing complexity and highly dynamic characteristics of Mobile Edge Computing (MEC) environments introduce considerable challenges for Deep Reinforcement Learning (DRL)-based task orchestration. With an increasing number of potential execution nodes, the set of available actions becomes substantially larger, leading to greater exploration complexity and exposing the learning agent to many infeasible or contextually inappropriate decisions. This paper proposes a Context-Aware Action Filter (CAAF) for Double Deep Q-Network (DDQN)-based task orchestration in MEC. CAAF dynamically evaluates candidate edge servers according to task characteristics, resource availability, network conditions, and system context. It combines a composite suitability assessment with an adaptive distribution-aware filtering mechanism that removes less-suitable actions when meaningful differences exist among candidates, while preserving action diversity when their suitability is comparable. The resulting reduced action space is then provided to the DDQN agent for task assignment. Simulation results demonstrate that CAAF-DDQN outperforms conventional DDQN, particularly under high-load conditions, reducing service time and task failure rate by approximately 20.0% and 45.7%, respectively. Scalability experiments with a larger number of edge servers further confirm its effectiveness under enlarged action spaces, achieving reductions of approximately 31.8% in service time and 89.5% in task failure rate without modifying the DDQN architecture or learning hyperparameters.	10.1109/TNSM.2026.3749201
Francesco Pizzato, Daniele Bringhenti, Fulvio Valenza	Parallelizing Automated Firewall Configuration via Traffic-Aware Decomposition	2026	Early Access	Firewalls (computing) Fluid flow Merging	Automating the configuration of distributed firewalls by refining high-level security requirements is a well-established approach to improve correctness and manageability in modern computer networks. However, existing solutions formulate this task as a single monolithic constraint-solving problem, whose scalability suffers as network size and policy complexity increase. To overcome this limitation, this paper proposes a traffic-aware decomposition approach for the automated, parallel configuration of distributed firewalls. The method decomposes the global refinement problem into cluster-scoped subproblems using a flow-weighted network representation and solves them concurrently before merging the results into a single deployable configuration. The approach is implemented on top of a constraint-based framework and evaluated on representative topologies. The results show substantial runtime reductions, with gains increasing with problem size and reaching up to ∼98% on larger instances, while maintaining satisfactory optimization quality. Moreover, the approach enables the resolution of firewall configuration problems beyond the limits of monolithic formulations.	10.1109/TNSM.2026.3747990
Ting-Yu Chang, Cooper Cheng-Yuan Ku	Two-tier Framework with AutoML for Detection and Classification of Multiple Types of Anomalies in Intrusion Attacks	2026	Early Access	Modeling Optimization Probability	Ensuring cybersecurity is paramount for protecting sensitive data and defending against increasingly sophisticated cyberattacks. Consequently, intrusion detection (ID) has become a critical area of research in network security. This paper proposes a two-tier framework that leverages efficient automated machine learning (AutoML) techniques to address data imbalance and optimize feature combinations for ID and classification of multiple types of anomalies. In the first tier, two complementary models are selected: a primary model optimized for overall dataset performance and a secondary model specifically chosen for its effectiveness on balanced data distributions. In the second tier, a probability stacking approach, supported by Out-of-Fold (OOF) cross-validation, integrates insights from the first-tier models. It acts as a binary safety net that successfully recovers rare minority attacks while preventing irreversible information loss. The proposed framework effectively mitigates the adverse effects of data imbalance while requiring only a minimal runtime memory footprint of approximately 44 MB (NSL-KDD) and a total process footprint of 165.52 MB (CIC-IIoT-2025), and achieves a rapid cold-start initialization of 0.85 seconds. These advantages make it well-suited for edge computing. Experimental results on the NSL-KDD dataset demonstrate that the proposed approach achieves an overall accuracy of 99.73%, outperforming many state-of-the-art methods by 0.4-20.6%. Furthermore, evaluations on the CIC-IDS-2017 dataset show a 6.3% accuracy improvement and a significant 36% increase in macro-average recall compared with a random-forest-only (RF-only) model. Moreover, extensive validation on the contemporary CIC-IIoT-2025 dataset reveals a significant 4.20% improvement in Macro F1-score over up-to-date baselines. Finally, evaluations on an independent set with novel attack types (KDDTest+) confirm strong generalization capability, achieving 77.10% accuracy and underscoring the framework's robustness in modern industrial scenarios.	10.1109/TNSM.2026.3747311
Miguel Familiar-Cabero	Zero-Parameter 3GPP Protocol Prior for Infrastructure-to-CNF Event Correlation in AI-Native Mobile Core Networks	2026	Early Access	Modeling Protocols 3GPP	A zero-parameter protocol prior derived from a 3GPP Release 19 knowledge graph constrains cross-attention between infrastructure and core-network event streams to the 50 admissible (event-type, procedure-step) pairs, removing 98.6% of candidates while preserving over 99% of causal links. Evaluated on 360 scenarios spanning six 3GPP procedure families (12 scenario types) and 631,748 protocol-admissible pairs from one commercial 5G Core deployment, the method achieves leave-one-type-out cross-validated macro AUC-PR 0.823 across heldout fault types and in-sample AUC-PR skill 0.972 (0.973±0.002 over 5 seeds). On the protocol-admissible candidate set (P), the protocol-constrained neural correlation model (PCNC) outperforms an unconstrained Transformer (skill 0.901), GNN-GAT (0.935), and two gated tabular baselines (logistic regression, 0.355; gradient-boosted trees, 0.737); a no-context ablation decomposes the gap: learned sequence embeddings account for 93% of the improvement over gradient-boosted trees (Δ = +0.218), while attention-routed context adds 7% (+0.017). Controlled ablations attribute the dominant discrimination to the protocol gate (Δ Skill = +0.077, p = 0.011 cluster bootstrap) and timer bounds (+0.084), not the encoder backbone (\|Δ\| ≤ 0.006, not significant). Leave-one-EI-type-out transfer confirms that the structural prior generalizes: 12 of 14 infrastructure event types score adequately without pattern-specific supervision (mean skill 0.708). Cross-operator robustness is validated by timer perturbation (skill ≥ 0.945 at 0.5–1.0× normative values) and single-procedure KG dropout (skill ≥ 0.968). Fail-closed behavior is confirmed empirically: novel infrastructure event types absent from the KG receive exactly zero correlation score.	10.1109/TNSM.2026.3749666
Ramazan Enisoglu, Burak Alp Inan, Muhammed Ali Erbir	MSCANet: A Multi-Scale Channel Attention Network for Encrypted Traffic Classification	2026	Early Access	Modeling Transformers Convolutional neural networks	Encrypted communication has rendered traditional traffic classification ineffective, creating challenges for QoS provisioning, network management, and security monitoring. Existing deep learning approaches exploit either spatial or sequence-level features in isolation, without jointly modeling multi-scale byte patterns and inter-packet ordering. We propose MSCANet, a hybrid architecture integrating a multi-scale 1D convolutional module, a sequentially applied Efficient Channel Attention (ECA) module, and a Transformer encoder, combining the two feature streams through a learnable weighted concatenation. With approximately 1.18M parameters, MSCANet attains 95.14% mean accuracy on the 20-class USTC-TFC2016 dataset and 92.61–98.01% across three 10-class sub-tasks over five random seeds, at a median per-sample inference latency of 1.29 ms on an NVIDIA RTX 5090 Laptop GPU. Against five baselines re-trained under an identical pipeline and schedule, spanning classical machine learning, CNN-only, attention-BiLSTM, and hybrid CNN–Transformer families, MSCANet is the most accurate on all four sub-tasks, improving over the strongest classical baseline by 6.16 pp and over the closest neural baseline by 2.38 pp while using 17.5% of its parameters and running 3.1× faster. These gains are demonstrated against the five re-trained baselines. Pretrained Transformers such as ET-BERT and YaTC report higher accuracy in their original publications, under their own preprocessing and with a pretraining stage that MSCANet does not use; the advantages we expect relative to them (no pretraining stage and, against ET-BERT, a model two orders of magnitude smaller) were not measured under our protocol, and we position MSCANet within the train-from-scratch regime.	10.1109/TNSM.2026.3748386
Mengyao Li, Sebastian Troia, Jean Pierre Asdikian, Giacomo Sguotti, Yingqian Zhang, Guido Maier	Hierarchical Auction-Based 5G Network Slicing with Region-based Multi-MNO Deployment	2026	Early Access	Resource management Modeling Radio access networks	Network slicing enables multiple virtual networks to share 5G network infrastructure while meeting heterogeneous service requirements. In parallel, region-based control in multi- Mobile Network Operators (MNOs) deployments has emerged, where each MNO manages a subregion of the network topology. This paper presents a two-level hierarchical combinatorial auction for end-to-end 5G Radio Access Network (RAN) slicing among MNOs, Mobile Virtual Network Operators (MVNOs), and users. At the upper level, MNOs provide slice resources to MVNOs. At the lower level, MVNOs allocate the slices to users. The framework considers dynamic demand, as requests may arrive at different times and remain active for different timeslots. A pair-bid mechanism is used to perform buyer–seller matching at both levels. The auction first selects the requests that can be accepted. The selection tries to keep the accepted bid value high without ignoring the electricity cost. After the winners are known, their payments are calculated using a VCG-like criticalvalue rule. The model also reflects the fact that different MNOs serve different regions and may have different infrastructure costs. In the simulations, the heuristic obtains higher welfare than both baselines. Simulations show that the heuristic improves welfare compared with two baseline methods, achieving up to 13.6% and 20.7% gains while maintaining scalability across different topologies.	10.1109/TNSM.2026.3747595
Aleksandar Avdalović, Joseph Khoury, Ibrahem AlJabea, Mustafa Hajij, Elias Bou-Harb	Beyond Flat Models: Preserving ICS Topology using Combinatorial Complexes for Hierarchy-Aware Intrusion Detection	2026	Early Access	Modeling Cells (biology) Integrated circuits	Industrial Control Systems (ICS) follow the Purdue model hierarchy, organizing sensors, actuators, Programmable Logic Controllers (PLCs), and supervisory control into distinct hierarchical layers. Despite this inherent structure, existing data-driven intrusion detectors still fail to model this intricate property. To address this gap, we introduce a Hierarchy-Aware Intrusion Detector, HAID, a topology-preserving framework that models an ICS as a Combinatorial Complex (CC), operating solely on process telemetry and trained on benign traces to detect falsified sensor measurements and unauthorized actuator commands. The complex captures devices, functional interactions, and PLC control zones within an ICS. CCs (unlike flat models) encode telemetry across hierarchical levels for training a Topological Neural Network (TNN). As such, HAID enables the embedding of the ICS Purdue model into the learning process, yielding anomaly scores and localization at each of the aforementioned levels. Evaluated on two water testbeds, SWaT and WADI, and on a simulated chemical process, TEP, HAID achieves competitive detection performance (82.7% F1 on SWaT and 50.1% on WADI, averaged over five runs), while providing explicit hierarchical localization unavailable in prior work. Through ablations, we confirm the added-value of hierarchical modeling, as removing the PLC-zone representation reduces F1 by 5.8 percentage points on SWaT and causes detection on WADI to collapse. The largest losses are on attacks that manipulate several devices at once, which device- and interactionlevel signals usually miss.	10.1109/TNSM.2026.3746967
Shubhani Aggarwal, Georges Kaddoum	QSB-SHA: Quantum-secured Blockchain for Smart Home Authentication	2026	Early Access	Authentication Smart homes Internet of Things	Today, IoT enables smart home users to remotely connect with household appliances via the Internet. Sensor-equipped smart devices are widely deployed in smart homes to collect and manage information regardless of user location. However, ensuring the secure and private accessibility of these devices in a centralized manner remains a major concern. The emergence of blockchain and quantum computing, with their enhanced information processing capabilities on decentralized platforms, offers a viable solution to this issue. Consequently, there is a need for cryptographic primitives that can withstand both quantum and classical attacks. To address this concern in the present study, we propose a quantum-secured blockchain (QSB) framework for smart home authentication (SHA). The proposed framework employs quantum key distribution (QKD) and maximally entangled states on a decentralized platform to enable secure key distribution and mutual authentication between smart home users and devices. Furthermore, our approach integrates quantum passwords and quantum digital signatures to verify and validate legitimate users, thereby ensuring security and privacy. The framework is simulated using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool for formal security analysis. We also conduct an informal security analysis and compare the performance of our proposed scheme with that of the previously proposed solutions. The results demonstrate that our framework outperforms existing approaches across various performance evaluation metrics.	10.1109/TNSM.2026.3747090
Tianle Lin, Lei Feng, Fanqin Zhou, Xudong Wang, Zhixiang Yang, Wenjing Li	Joint AP Selection and Power Allocation in Satellite–Terrestrial Cell-Free Network with Heterogeneous Multi-Agent Learning	2026	Early Access	Satellites Resource management Low earth orbit satellites	While Low Earth Orbit (LEO) satellites are vital for 6G non-terrestrial networks (NTN), applying Cell-Free Massive MIMO (CF-mMIMO) to high-mobility constellations remains challenging despite its capability to resolve conventional handover and interference issues. The traditional centralized processing model becomes impractical due to prohibitive latency, signaling overhead, and outdated Channel State Information (CSI), which degrades system performance. To overcome this challenge, we design a LEO Satellite Cell-Free Massive MIMO (LCF-mMIMO) system. In this system, each satellite independently determines its transmit power within the serving cluster, while statistical CSI is utilized to counter the fast-varying instantaneous CSI resulting from the rapid movement of LEO satellites. Additionally, to solve the minimum UE quality of service(QoS) maximization problem with reduced latency and signaling overhead, we develop a Heterogeneous Multi-Agent Proximal Policy Optimization (H-MAPPO) algorithm for joint Access Point (AP) selection and power allocation. In this algorithm, User Equipments (UEs) and satellites operate as heterogeneous agents: UEs perform AP selection based on local service demands, while satellites allocate power according to local power and interference states. Their decisions are coupled through the AP selection matrix, which converts UE-side access results into satellite-side associated UE sets and limits information exchange to selected UE-satellite associations. Simulation results demonstrate that the proposed scheme effectively adapts to dynamic LEO environments and achieves superior User QoS over learning-based benchmarks. Compared with the centralized SR-JCPA method, H-MAPPO maintains a comparable minimum QoS of 0.52 versus 0.53 under 16 UEs, while reducing the per-episode execution time from 17646.81 s to 96.81 s.	10.1109/TNSM.2026.3747765
Yifan Zeng, Zihui Gu, Peijia Zheng, Huiyu Zhou	A Federated Semi-Supervised Framework for Data Efficiency and Privacy-Preserving Intrusion Detection in Internet of Things	2026	Early Access	Internet of Things Modeling Labeling	The Internet of Things (IoT) forms the foundation of modern smart environments, enabling intelligent industries, homes, cities, healthcare, and autonomous systems. However, as IoT devices increasingly connect to cloud platforms and the Internet, they become highly vulnerable to network intrusions that can lead to critical data breaches and service disruptions. This paper proposes a Contrastive Federated Semi-Supervised Learning Network Intrusion Detection framework (CFedSSL-NID), specifically tailored to enhance the security of IoT communications. It addresses practical scenarios where clients lack labeled data and computational resources, and where data privacy is paramount. The CFedSSL-NID framework enhances detection performance and robustness. It uniquely achieves this by synergistically leveraging abundant, unlabeled data from distributed IoT gateways through self-supervised contrastive learning, while optimally integrating supervised signals from a central server. This dual approach enables highly accurate intrusion detection while substantially mitigating the reliance on scarce and costly labeled data. Experiments on the TON-IoT, UNSW-NB15, and NSL-KDD datasets show that CFedSSL-NID outperforms federated and centralized baselines; additional evaluations under Non-IID data, asynchronous participation, and malicious-client updates confirm robustness, while communication and edge-inference analyses indicate its deployment feasibility.	10.1109/TNSM.2026.3742261
Aymen Daoud, Md. Zoheb Hassan, Georges Kaddoum	SpectraTwin: Generative Intelligence-Powered Digital Twin for Dynamic Spectrum Awareness	2026	Early Access	Modeling Radio frequency Labeling	Spectrum monitoring and classification under evolving RF spectrum landscapes are key enablers of spectrum awareness for dynamic spectrum management and interference detection in 6G networks. Although artificial intelligence (AI) and machine learning (ML) techniques have shown promising performance in spectrum classification, most existing models are static, non-generative, and optimized for specific training environments. As a result, channel distribution shifts, model brittleness, and the scarcity of labeled RF data for fine-tuning AI/ML models lead to significant performance degradation in evolving RF spectrum landscapes. In this context, digital twin (DT) provides a promising approach by maintaining a virtual representation of the RF spectrum environment that supports what-if analysis, data generation, and iterative model updates. In this paper, we propose SpectraTwin, a multi-AI-enabled, adaptive, and self-evolving DT framework for multi-timescale spectrum classification under evolving RF spectrum states and incrementally increasing task complexity. SpectraTwin integrates three tightly coupled modules: (i) a Dynamic Mask Generative Adversarial Network (GAN) that serves as the virtual replica of the RF spectrum environment, synthesizing realistic, classand channel-conditioned spectrograms on demand to mitigate RF data scarcity; (ii) a continual learning-based spectrum classification model that incrementally adapts to evolving RF environments while preserving prior knowledge; and (iii) an active-learning-inspired orchestration module that decides when to adapt, which classes to synthesize, and which exemplars to retain, so that model updates are performed in response to detected change rather than on a fixed retraining schedule. By jointly combining generative, continual, and active learning, SpectraTwin enables robust spectrum awareness through (a) faithful emulation of RF spectrum evolution, (b) detection of domain distribution shifts, and (c) efficient incremental adaptation under non-stationary conditions while maintaining a favorable balance between accuracy and forgetting. Simulation results on both custom dynamic RF datasets and multiple benchmark datasets (consisting of simulated and real-world signal captures) show that SpectraTwin achieves superior adaptability and classification performance compared with existing approaches.	10.1109/TNSM.2026.3743810
Messaoud Ait-Yahia, Wael Jaafar, Rami Langar	Joint Design of Blockchain-Enabled Service Placement and Task Assignment in Vehicular Fog Computing Networks	2026	Early Access	Delays Timing Optimization	Driven by the evolution of blockchain and fog computing, vehicular networks are increasingly capable of supporting latency-sensitive applications with enhanced security and trust guarantees. However, the joint resource allocation for task offloading and blockchain services has been insufficiently investigated in existing works. To address this gap, this paper proposes a framework for jointly allocating resources of blockchain, users’ virtualized services, and Mobile Edge Computing (MEC) task assignment in Vehicular Fog Computing (VFC) networks. Specifically, we formulate the optimization problem as an integer nonlinear programming model aiming to maximize the satisfaction rate of users’ service requests while minimizing the corresponding blockchain operation time under mobility, queuing, instantiation, and resource constraints. To solve it in a timely manner, we design two-stage hierarchical low-complexity solutions, namely a Particle Swarm Optimization-based Joint Blockchain-enabled Service placement and Task Assignment algorithm (PSO-JBSTA), and a Genetic Algorithm-based approach (GA-JBSTA). Through extensive simulations, we demonstrate the effectiveness of PSO-JBSTA (resp. GA-JBSTA) and their adaptability to network conditions, achieving an average 35% (resp. 24%) improvement in users’ service satisfaction rate and 9.5% (resp. 10.2%) reduction in average blockchain validation delay compared with the baselines.	10.1109/TNSM.2026.3737068
Ahlem Menaceur, Hamza Drid, Mohamed Rahouti, Yasmine Medjadba, Abdellah Chehri	Priority-Aware Dual-Queue Buffering for Reactive SDN Fault Management with Reduced Loss	2026	Early Access	Software defined networking Joining processes Loading	Software-Defined Networking (SDN) provides controller-driven flexibility for network and service management, yet reactive failure recovery often suffers from a recovery gap between fault detection and the installation of updated forwarding rules. Packets arriving during this interval are vulnerable to loss, while rerouted flows may overload specific links, undermining service continuity. To address these challenges, this paper proposes a dual-priority, queue-assisted reactive recovery mechanism that integrates traffic classification and buffering at the data plane. The design assigns flows to a high-priority queue (HPQ) or a low-priority queue (LPQ), ensuring that critical traffic is preserved while the controller completes rerouting. Evaluation on a 9-switch custom topology and the 16-node NSFNET backbone demonstrates that the proposed approach reduces packet loss (from 10.3% to 5.4% in the 9-switch topology and to below 1.9% in NSFNET) and improves post-failure load distribution, decreasing congested and surcharged links by 37.6% and 20.6%, respectively, compared to conventional reactive recovery. These results highlight that priority-aware buffering during the recovery gap enhances resilience, scalability, and operational efficiency for SDN fault and service management.	10.1109/TNSM.2026.3744288
Ashesh Gaur, Sridhar Radhakrishnan, Mohammed Atiquzzaman	Inverse Packet Classification: Reconstructing Behaviorally Equivalent Rule Sets from Observed Traffic	2026	Early Access	Merging Labeling Modeling	Network devices classify packets with ordered rule tables, but those tables are often inaccessible. Passive reconstruction can support behavior audits, misconfiguration analysis, and policy compliance without privileged device access. We define inverse packet classification as inferring a compact rule set from observed packet headers and device actions. The goal is observational equivalence on observed traffic, not recovery of the original configuration or priority order. The problem is difficult because traffic covers only part of the header space. Packet classifiers also use first-match semantics and structured prefix or interval predicates. We propose InversePC, which combines entropy-guided k-d-tree partitioning with greedy region merging. For a dominant default action, InversePC-HiB uses a hybrid split loss, exception-only merging, and a wildcard default rule. On synthetic classifiers, accuracy reaches 100% once coverage is sufficient. On native-resolution ClassBench filters, InversePC reaches 94.1% action agreement versus 88.3% for cross-validated CART on 1,000-rule sets. At 8-bit quantization, the two methods are statistically tied (98.1% versus 98.0%). In a well-covered 250-rule setting, InversePC uses 1.20× the original rule count versus 1.32× for CART. HiB is most useful for sparse, irregular default-permit ACLs, where it uses up to 2.5× fewer rules than InversePC at 99.8% accuracy. Optimized implementation makes native-resolution reconstruction practical in seconds.	10.1109/TNSM.2026.3744323
Shyam Kumar Shrestha, Shiva Raj Pokhrel, Jonathan Kua	Adapting Large Language Models for TCP Fairness over Wi-Fi	2026	Early Access	Large language models TCP Modeling	Designing Transmission Control Protocol (TCP) Congestion Control Algorithms (CCAs) for heterogeneous Wi-Fi networks remains a challenge due to rapidly varying delay, loss, and contention dynamics. These effects often lead to CCA incompatibility, flow unfairness, and starvation among competing TCP flows. Although Deep Reinforcement Learning (DRL) has shown promise in mitigating these challenges, its slow convergence, high training costs, and limited generalization hinder practical deployments. In this paper, we propose TCP-LLM, a learning-augmented transport framework that leverages Large Language Models (LLMs) for compatibility-aware CCA selection. TCP-LLM encodes multivariate TCP time-series into token embeddings and performs single-step discrete CCA decisions through a lightweight LLM decision head. Parameter-efficient low-rank adaptation (LoRA) reduces the number of trainable parameters by approximately two orders of magnitude. Evaluation using traces collected from a physicalWi-Fi testbed with competing TCP Cubic, Bottleneck Bandwidth and Round-trip propagation time (BBR), and Performance-oriented Congestion Control (PCC) flows shows that TCP-LLM achieves the highest mean, geometric-mean, and minimum per-flow throughput among the evaluated schemes. TCP-LLM also exhibits faster and more stable training convergence and supports low-latency bounded inference. These results demonstrate the feasibility of LLM-based sequence adaptation for compatibility-aware transport-layer supervision under heterogeneous wireless conditions.	10.1109/TNSM.2026.3739364
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
Qaisar Ali, Ihsan Ullah, Youn-Hee Han	Topology-Preserving Virtual Network Embedding Using Persistent Homology and Graph Attention Reinforcement Learning	2026	Early Access	Substrates Topology Joining processes	Virtual Network Embedding (VNE) maps virtual network requests (VNRs) onto shared substrate networks while satisfying node, link, and service constraints. Existing VNE methods mainly optimize resource-centric objectives such as CPU utilization, bandwidth consumption, acceptance ratio, and revenue, but often treat structural service patterns as implicit connectivity requirements. However, patterns such as redundancy loops, hierarchical aggregation, centralized control, and fault-tolerant structures may encode critical service logic, and their degradation can reduce reliability and violate service-level guarantees. This paper proposes TP-VNE, a topology-preserving and pattern-aware VNE framework that integrates persistent-homology-assisted structural characterization, graph attention-based representation learning, and proximal policy optimization. Persistent homology extracts multi-scale topological signatures from VNRs and substrate candidate regions, while complementary graph features distinguish acyclic structures such as stars and trees. A graph attention network learns topology-aware node embeddings by combining resource and structural features, and a PPO agent performs sequential node and link mapping under feasibility-aware action masking. Finally, a topology verification module validates the path-induced embedded subgraph before resource commitment. Simulation results show that TP-VNE improves the acceptance ratio from 48.5% to 83.5% at λarr = 0.20 and increases long-term average revenue from 2,780 to 4,980 compared with Baseline PPO, while achieving 89.3% pattern preservation accuracy with acceptable embedding overhead. Scalability experiments on GT-ITM substrates with up to 1000 nodes further demonstrate the robustness of TP-VNE under increasing network size.	10.1109/TNSM.2026.3744368
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