TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Mohammad Saberi, Ali Ahmadi Esfidi, Mahdi Dolati, Naeimeh Omidvar	FlexINA: In-Network Aggregation for Accelerating Distributed Machine Learning with Flexible Routing	2026	Early Access	Modeling Switches Timing	The growing scale and complexity of modern machine learning (ML) models have rendered distributed training indispensable. However, as model dimensions expand, communication overhead increasingly dominates the overall training time, creating a critical performance bottleneck. While techniques such as gradient compression offer partial relief, they often incur substantial computational overhead or require specialized hardware. In-network aggregation (INA) has emerged as a compelling alternative, offloading partial computations—such as gradient reduction—directly onto programmable network switches. Yet, current INA frameworks predominantly rely on rigid, static routing schemes or uncoordinated, opportunistic aggregation, fundamentally limiting their capacity to exploit the full potential of programmable data planes. To address this limitation, we formulate the Joint Scheduling-Routing for In-Network Aggregation (JINA) problem. JINA introduces a time-slotted execution model to enable fine-grained temporal scheduling synchronously with spatial routing. By leveraging source routing, it dictates perpacket forwarding paths while explicitly coordinating multi-hop partial aggregation across the network. Furthermore, we propose FlexINA, a scalable optimization framework that solves the JINA problem through a three-phase dimensionality reduction strategy: host-level pre-aggregation, topological switch filtration, and temporal window partitioning. Extensive evaluations across fifteen diverse network topologies demonstrate that FlexINA reduces the total network load by up to 8.3% compared to INA baselines, while maintaining high computational efficiency.	10.1109/TNSM.2026.3740503
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
Jian Ye, Gaolei Fei, Jiajie Li, Xuemeng Zhai, Guangmin Hu, Ming Liang	Network topology inference via time-frequency consistency analysis of end-to-end traffic time series	2026	Early Access	Timing Topology Measurement	In this paper, we study the classical network tomography problem of tree topology inference using end-to-end measurement. Traditional methods rely on time series correlations to measure shared path lengths. However, time-varying and non-stationary background traffic often biases these measurements. To overcome this, we comprehensively explore end-to-end traffic time series via time-frequency consistency analysis. Specifically, we jointly characterize time series in both time and frequency domains, imposing a consistency constraint. This ensures embeddings of the same time series across domains remain close in the latent space, providing a robust description of traffic dynamics. Our contrastive learning approach effectively highlights similarities among flows on common paths and distinguishes those on disjoint paths, thereby mitigating background traffic interference. Extensive evaluations across widely used probing strategies and metrics demonstrate that our embedding significantly enhances time series correlation and accurately measures shared paths.	10.1109/TNSM.2026.3749558
Jian Liu, Jinhong Chen, Eldar Sabanovic, Jinliang Liu, Engang Tian, Oh-Min Kwon	Predictor-Enhanced Constrained Reinforcement Learning for Networked CAV Platoons: A Dual-Redundancy Scheme	2026	Early Access	Timing Vehicles Learning (artificial intelligence)	This paper investigates resilient tracking control for connected and automated vehicle (CAV) platoons under false data injection (FDI) attacks and prescribed spacing constraints. A kinematics-based state predictor is combined with a dynamic event-triggered mechanism (ETM) to provide smooth trajectories from sparse, FDI-corrupted measurements while reducing communication use. Prescribed performance control (PPC) maps the predicted spacing error into an unconstrained state for continuous-time actor-critic reinforcement learning (RL). The actor-critic controller learns an approximate tracking policy without identifying the unknown nonlinear drift. A locally sensed, saturated proportional-derivative fallback controller can override the learned command when the measured spacing error approaches the prescribed boundary. These two control paths form the dual-redundancy scheme. The analysis excludes finite-time accumulation of triggering instants, establishes input-to-state stability of the prediction-error subsystem, and gives conditional ultimate bounds for the prediction error and sampled adaptive weights under the stated input and approximation-domain conditions. Matched ablations and multi-seed tests evaluate communication use, fallback activation, and the responses to scalar and vector attacks.	10.1109/TNSM.2026.3748969
Qu Wang, Hao Wu, Xin Luo	A Highly Accurate Approach to Dynamic Network Representation Learning Based on Biased Nonnegative Tensor Ring Factorization	2026	Early Access	Modeling Tensors Ranking (statistics)	A large-scale dynamic network can be represented as a high-dimensional and incomplete (HDI) tensor, whose numerous entries are missing because it is impossible to acquire all interactions among massive numbers of nodes. Tensor network-based models have emerged as powerful tools for HDI tensor representation learning. Tensor ring (TR) decomposition represents a high-order tensor as a cyclic interconnection of third-order tensors, providing a compact tensor network structure for latent representation learning. However, conventional TR decomposition is designed for complete tensors and thus does not account for the substantial missing data in HDI dynamic networks, while existing TR-based representation learning models also have limited ability to model multidimensional link fluctuations. To address these issues, this work proposes a biased nonnegative tensor ring factorization (BNTRF) model with three main components: a) adopting a data-density-oriented modeling strategy that focuses computation on observed entries, thereby reducing the modeling cost of HDI tensors; b) designing a linear bias mechanism to capture multidimensional link fluctuations, thereby improving model stability and representation accuracy; and c) developing a self-adaptive parameter learning scheme to improve computational efficiency. Experimental studies on six real dynamic networks demonstrate that BNTRF outperforms several state-of-the-art models in learning accuracy while incurring lower computational costs.	10.1109/TNSM.2026.3748838
Yule Han, Kezhi Wang, Yizhe Zhao, Kun Yang	Smart-TCP: An Agentic AI-based Autonomous and Adaptive TCP Protocol	2026	Early Access	Modeling Protocols TCP	The Transmission Control Protocol (TCP) relies on deterministic state machines and sequence-number arithmetic to ensure reliable communication. However, hard-coded protocol logic is difficult to adapt to increasingly complex and dynamic network conditions. This paper proposes Smart-TCP, an agentic AI-based transport protocol framework that organizes TCP control logic as a fast/slow model-assisted decision process. Specifically, a feature-aware classifier routes routine segments to a lightweight Small Language Model (SLM) fast path and anomalous or boundary cases to a Large Language Model (LLM) slow path, while an Arithmetic Logic Unit (ALU) handles deterministic computation of sequence and acknowledgment numbers. A state module maintains connection state, packet history, and key control variables, enabling protocol decisions over the full session context. A scoped Protocol Guard checks candidate decisions against RFC-derived rules before execution. We evaluate Smart-TCP through path classification, atomic packet processing, slow-path anomaly response, and end-to-end session interaction. Experimental results show that Smart-TCP achieves 99.14% action accuracy on 700 slow-path anomaly and fallback samples, and a 100% full-lifecycle success rate over 300 ideal fast-path end-to-end session windows. The ordinary 32-KiB transfer tests achieve a 96.5% completion rate at 20% segment loss. These results suggest that separating model-based protocol decisions from deterministic arithmetic improves the reliability of TCP control for network service operation.	10.1109/TNSM.2026.3747846
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
Muhammad Faizan Ayub, Xiong Li, Khalid Mahmood, Muhammad Asad Saleem, Salman Shamshad, Zahid Ghaffar, Yong Xie	A Lightweight and Secure Key Exchange Protocol for fortifying Home Area Network in Smart Grid	2026	Early Access	Protocols Smart grids Authentication	Smart grid networks rely on communication links to support monitoring and control of energy usage. Because these links are open, especially in Home Area Networks (HAN), security problems can occur. Devices may be physically captured, attackers may impersonate legitimate nodes, and temporary secret values may be exposed. Several key exchange protocols have been proposed to address these concerns. In practice, some of them still suffer from security weaknesses, while others introduce high communication and computation costs. In this work, we present a key exchange approach for HAN in smart grid systems based on Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUF). The protocol is analyzed using the Random-or-Real (RoR) model, along with an informal security discussion. Different attack scenarios, including impersonation, disclosure of stored meter data after capture, and ephemeral secret leakage, are considered. We also compare the proposed protocol with existing protocols and based on the comparison results, the proposed protocol shows a reduction of 3.843% in computational cost and 29.49% in communication cost.	10.1109/TNSM.2026.3746724
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
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
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
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
Chengsheng Pan, Yingzhi Wang, Huaifeng Shi, Lishang Qin, Xiaosong Cui	DBSDD-AQM: Dynamic Buffer Sizing-Based Deep Deterministic Active Queue Management for Edge IoT Networks	2026	Early Access	Delays Satellite broadcasting Internet of Things	Bursty heterogeneous traffic can build persistent queues at edge IoT gateways when the offered load converges on a lower-rate bottleneck. Conventional active queue management (AQM) regulates packet dropping under a fixed buffer capacity, whereas adaptive buffer sizing alone does not coordinate capacity adjustment with queue-removal control. We propose Dynamic Buffer Sizing-Based Deep Deterministic Active Queue Management (DBSDD-AQM), which combines a bounded buffersizing loop with a faster continuous queue-removal controller. DBS adjusts the admissible capacity according to sustained queue evolution, while the DDPG Actor selects packet-granular removal actions for the resident backlog. A class-aware extension maps three concurrent service classes to logical FIFO queues sharing the same dynamic physical buffer. DBSDD-AQM is implemented using an NS3-PyTorch framework. Aggregate experiments compare it with RED, ARED, PIE, CoDel, and DQN-AQM, while the class-aware evaluation additionally includes FQ-CoDel and FQ-PIE. Matched-DBS comparisons show similar throughput but different delay–loss operating points among the aggregate controllers, and component ablation distinguishes the effects of capacity adaptation and learned queue removal. Relative to a fixed 50 KB buffer, DBS reduces average queueing delay by 16.23% for UDP and 17.70% for TCP Westwood in controlled transport experiments. In the evaluated three-class overload scenarios, all classes retain nonzero throughput. Class-aware DBSDD-AQM provides stronger D- and T-class differentiation, whereas FQ-CoDel and FQ-PIE produce more uniform class-level outcomes and stronger R-class performance. The measured 95th-percentile batch-one Actor inference latency is 0.109 ms on the reported x86-64 platform.	10.1109/TNSM.2026.3741334
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
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
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
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
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