TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Rosario Patanè	Dynamic Coalition Formation Under Capacity Constraints: A Regime-Switching Value-Investing Game	2026	Early Access	Games Modeling Resource management	Edge computing service deployment requires the Network Operator (NO) and Service Providers (SPs) to coordinate investment and resource allocation under uncertain demand. This paper develops an audit-based service management framework for daily coalition formation with fixed capacity and service-level agreement (SLA) reservations. A two-stage coalitional game separates infrastructure investment from operational participation. Demand follows a Geometric Random Walk (GRW), and SPs apply an ordered admission and suspension rule using reported marginal contributions. With the NO held fixed, the operational coalition value is submodular; mandatory reservations can produce negative Shapley payoffs. We derive a separable benchmark, compute operational Shapley allocations, and give a sufficient condition for convergence to an individual equilibrium. A probabilistic audit mechanism provides a conditional daily truthfulness guarantee under verified settlement and enforceable penalties. For five service classes over three years, the reference run gains 12.4% over static grand-coalition membership and recovers 94.6% of the full-information optimum. Across 30 runs, the median gain is 13.3%. Sensitivity tests examine policy parameters, capacity and alternative demand models. The gain depends on capacity and update order; abundant capacity can make the admission rule underperform static membership.	10.1109/TNSM.2026.3750592
Byungchang Kim, Shinyoung Lue, Seonyeong Hwang, Youhee Park, Yeji Lee, Jaemin Jeong, Peter Bloodsworth, Euiseok Hwang	GRIDS: Generalized Representation-based Intrusion Detection System via Federated Contrastive Learning	2026	Early Access	Modeling Labeling Intrusion detection	Existing AI-based intrusion detection systems are typically trained for specific network environments, leading to significant performance degradation when deployed in different settings or exposed to evolving attack strategies. We propose GRIDS (Generalized Representation-based Intrusion Detection System), a federated contrastive learning framework for cross-environment intrusion detection that keeps raw traffic data local. GRIDS employs a Transformer-based encoder trained with supervised contrastive learning across distributed network environments, learning a shared representation without exchanging sensitive traffic data. The encoder embeds network flow features into a latent space where semantically similar traffic patterns cluster together, enabling effective anomaly detection via k-nearest neighbor scoring. Evaluated on four heterogeneous NetFlow datasets, GRIDS outperforms existing federated IDS baselines on average with a single unified model. In leave-one-out cross-validation, a substantial portion of the detection capability of GRIDS transfers to network environments unseen during training, and the level of transfer varies across target networks. Deployment at such a network requires no labeled attack data or fine-tuning. It uses the benign traffic of the network as the kNN reference set and its unlabeled traffic for normalization and threshold calibration. The framework targets deployments in which a single model protects multiple heterogeneous networks.	10.1109/TNSM.2026.3748922
Shengye Gong, Hui Yang, Qiuyan Yao, Xin Li, Tiankuo Yu, Wenxin Liu, Yang Zhao, Yucong Liu, Jie Zhang, Mohamed Cheriet	Resilient Hierarchical Polyhedron-based Configuration Protection for Large-Scale Multi-Domain Optical Networks	2026	Early Access	High performance computing Joining processes Protection	Large-scale multi-domain optical networks are increasingly exposed to spatially correlated and concurrent failures, while monolithic pre-configured protection structures incur rapidly increasing global search complexity and protection-resource costs under heterogeneous physical constraints. To address this challenge, this paper proposes a resilient Hierarchical Polyhedron-based Configuration (HPC) architecture that decomposes a monolithic global survivability requirement into adaptive intra-domain connectivity constraints and a compact inter-domain connectivity constraint. To alleviate resource imbalance caused by topology-only network partitioning, a Topology-Resource Aware Domain Partitioning (TRADP) algorithm is developed to construct protection-supportable sub-domains by jointly considering topological structure and physical resource distribution. To avoid imposing a rigid protection level on heterogeneous domains, HPC assigns adaptive intra-domain connectivity targets according to local failure exposure and physical resource supportability. To reduce the complexity of cross-domain protection while preserving physical feasibility, a super-node abstraction and physical bridge-link mapping mechanism is employed to construct the inter-domain protection backbone under capacity, spectrum, and SRLG constraints. Extensive evaluations on networks of up to 1000 nodes and representative GEANT and NSFNET topologies demonstrate that HPC-TRADP maintains a recovery rate of 92.94% at 1000 nodes and 85.82% even when 30% of physical links fail concurrently. It further reduces protection overhead by up to 79.60% relative to p-KECS while maintaining efficient recovery paths, demonstrating a favorable balance between survivability, resource efficiency, and scalability.	10.1109/TNSM.2026.3750417
Ivette Kembely Carrera Manosalvas, Paulo Freitas De Araujo-Filho, Georges Kaddoum, Hadi Otrok	Unsupervised Hybrid Quantum-Classical Generative Adversarial Network-Based Intrusion Detection System for 6G-enabled Internet of Things	2026	Early Access	Modeling Signal detection Generative adversarial networks	The emergence of sixth-generation (6G) mobile networks contributes to the widespread adoption of critical Internet-of-Things (IoT) applications, such as telesurgery and autonomous vehicles. However, such advancements expand attack surfaces and make traditional security mechanisms insufficient. While machine learning (ML)-based intrusion detection systems (IDSs) have been increasingly adopted to detect attacks in such heterogeneous and dynamic environments, their complex architectures often result in high computational costs and latency, making them unsuitable for the real-time demands of 6G-enabled IoT applications. To address these challenges, we propose a novel unsupervised quantum generative adversarial network (QGAN)-based IDS. Our solution enhances the GAN generator’s ability to model complex data distributions by combining a parametrized quantum circuit with a classical neural network (NN), while maintaining a lightweight classical discriminator (fewer than 2.000 parameters) at the edge. This design significantly reduces computational complexity and minimizes detection times, since only the classical discriminator is used during inference. Experimental results demonstrate that our solution achieves competitive detection rates with an ultra-low inference latency of 0,22 ms, outperforming state-of-the-art GAN-based and transformer-based IDSs by over 2,81 times. Therefore, our architecture provides a resource-efficient security framework that delivers the ultra-low latency and high detection rates essential for next-generation IoT applications.	10.1109/TNSM.2026.3747752
Mayank Ramnani, Shasank Dixit, Sushil Yadav, Saad Ahmed, Sidharth Sharma	Split-Aware Function Placement with Availability Guarantees and Optical Provisioning in vRANs	2026	Early Access	Radio access networks 5G mobile communication Joining processes	The rapid evolution of beyond-5G and emerging 6G networks is driving the need for flexible, reliable, and cost-efficient virtualized Radio Access Network (vRAN) architectures capable of supporting heterogeneous services such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communication (mMTC). Future disaggregated RAN systems are expected to rely heavily on network slicing, functional split flexibility, and optical x-haul infrastructures to support stringent performance, scalability, and availability requirements. In this paper, we present an integrated framework for reliable, slice-aware, and functional split-aware Virtual Network Function (VNF) placement with lightpath provisioning in disaggregated vRAN environments. The proposed approach maximizes mobile network operators’ profit by jointly optimizing function placement and optical resource allocation under latency, processing, bandwidth, and availability constraints. We formulate the problem as an Integer Linear Programming (ILP) model with two variants: one that employs unshared backups and another that uses a more cost-efficient shared backup scheme. To address ILP complexity, we develop a heuristic algorithm and a GA-based metaheuristic that provide computationally efficient alternatives for larger instances. Extensive evaluations on topologies up to 128 nodes show that shared backup variants yield up to 18% higher profit, while maintaining up to 5–10% lower normalized CPU usage than unshared counterparts.	10.1109/TNSM.2026.3751852
Jiawei Dong, Mohsen Ghafouri, Chadi Assi	Cybersecurity Risks in Electric Vehicle Charging Management Systems	2026	Early Access	IP networks Security Internet of Things	Electric Vehicle Charging Stations (EVCSs) have become the heart of electrified transportation infrastructure. Their increasing Internet exposure makes them high-value targets for cyberattacks with potential implications for grid stability. This study presents a comprehensive, data-driven security analysis of globally exposed EV Charging Management Systems (EVCMS), bridging theoretical vulnerability concerns with quantified real-world threats. We discover and assess Internet-exposed EVCMS infrastructure, then deploy multi-component honeypot systems to capture live attack intelligence. Our novel EVCS discovery technique identifies 59,359 EVCMS IPs from 798 vendors, with notable improvements in non-English-speaking regions, overcoming traditional scanning limitations. Security assessment of 17 vulnerability types reveals most critical vulnerabilities remain unaddressed three years after coordinated disclosure. Darknet intelligence analysis reveals 10,647 unique IPs that also appeared in our EVCMS dataset participating in unsolicited scanning throughout 2024 with periodic surges. We deploy a comprehensive honeypot system with proactive engagement that actively induces compromised EVCMS devices to attack our infrastructure, spanning two monitoring phases: a ten-day period with proactive engagement and an eight-month period of long-term passive monitoring. We observed generic IoT threats and found no EVCMS-specific malware in the samples examined. A small portion of human exploration activities is found in the web portal honeypot. We observed liveness probes on both the Linux server honeypots and the OCPP backend, serving as a critical warning of imminent threats demanding immediate vendor attention before escalation to destructive campaigns.	10.1109/TNSM.2026.3751489
Yichen Zhang, Di Chen, Kaijie Zhu, Zekun Tao, Wei Lin, Rui Zheng	Beyond Valley-Free: Transformer-based AS Path Inference Using Path Embedding	2026	Early Access	Border Gateway Protocol Routing Ranking (statistics)	Gaining insight into autonomous system (AS)-level paths is essential for understanding Internet routing behaviors and enhancing inter-domain routing security. Due to increasingly complex Internet topology and limited measurement access (e.g., router-side ICMP restrictions), few AS paths can be accurately observed, making AS path inference a necessity. However, most existing inference methods are developed based on the valley-free constraint. This limits their ability to capture real-world dynamic routing behaviors. In this paper, we propose PathRank, a Transformer-based method that infers AS paths from a source-AS to a destination-prefix through probability-based ranking, thereby closely reflecting real-world inter-domain routing complexities. From observable BGP data, PathRank generates candidate paths, encodes them as embeddings for a self-attention mechanism to capture cross-hop dependencies, and employs a prefix-length-weighted loss function during training to reflect real-world traffic impacts.We conduct experiments on large-scale public BGP datasets covering IPv4 and IPv6. PathRank places the observed BGP path among the top-10 ranked candidates in 96.82% (IPv4) and 96.65% (IPv6) of test cases, outperforming the strongest external baseline by 4.20 and 11.72 percentage points, respectively. On non-valley-free cases, where valley-free-constrained methods degrade significantly, PathRank maintains inference performance.	10.1109/TNSM.2026.3752819
Junxiong Lin, Xiuyi Zhao, Pan Li, Tian-Yu Zuo, Mingyi Liu, Hailiang Zhao, Yichuan Jiang, Jiuchuan Jiang, Kai Di, Ning Luo	Hallucination-Aware Teaming for Reliable Service Composition in LLM-Driven Industrial Agent Networks	2026	Early Access	Modeling Large language models Educational institutions	Large-language-model-driven (LLM-driven) industrial agents increasingly exchange intermediate results through service-composition pipelines, so an upstream hallucination can corrupt several downstream decisions. We formulate hallucination-aware teaming (HATP), which jointly considers capability coverage, distinct-base-family redundancy, resource budgets, and chain-level error propagation, and prove that its deterministic special case is NP-hard. We then propose HAT, a polynomial-time heuristic that combines master-driven recruitment with self-consistency, cross-agent agreement, external-constraint signals, and bounded fallback. In five simulator settings with 200 paired trials each, HAT has the highest mean Acc and lowest mean HPL in four settings; LLM Debate leads in Easy, and the HAT–Debate Acc differences in Easy and Complex are not significant after Holm correction. A 100-instance exact-enumeration study yields a 0.95% median relative utility gap and an approximately 10× median speedup for the tractable HATP instantiation with upstream discount and fallback disabled and a fixed scalar utility with normalized propagation length. Paired ablations show clear contributions from Phase B, bounded fallback, and the combined trust mechanism, whereas most individual trust-signal effects overlap with uncertainty. In a fixed 50-task live-LLM check using five 8B–30B model families, HAT attains Acc 0.280 versus 0.300 for Beta-Reputation and does not lead every comparator on propagation length. The live study is therefore a limited test outside the simulator and does not show that HAT is generally better in deployment.	10.1109/TNSM.2026.3752977
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
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
Mario Chahoud, Hani Sami, Ahmad Hammoud, Rabeb Mizouni, Hadi Otrok, Jamal Bentahar, Azzam Mourad, Chamseddine Talhi	Multi-agent Deep Reinforcement Learning Scheme for On-Demand Federated Learning	2026	Early Access	Modeling Federated learning Training	The rapid growth of intelligent IoT devices and mobile edge infrastructures has generated massive decentralized data, necessitating efficient and privacy-preserving learning. Federated Learning (FL) enables distributed model training without sharing raw data; however, its performance degrades in dynamic mobile environments due to unpredictable availability, stragglers, and frequent dropouts. In On-Demand FL, training services and models are deployed only when needed rather than relying on always-active infrastructure, requiring orchestrators to adapt to client arrivals, departures, and mobility while efficiently allocating limited resources and maintaining training continuity. Existing client-selection methods rely on static heuristics or centralized Reinforcement Learning models that struggle to scale and adapt to such dynamics. To address these challenges, we propose a cooperative Multi-Agent Deep Reinforcement Learning (MADRL) framework for client selection in On-Demand FL. Multiple orchestrators act as agents under a Centralized Training and Decentralized Execution (CTDE) paradigm. The problem is formulated as a Partially Observable Markov Game (POMG), capturing mobility, partial observability, and distributed decision-making. A mobility-aware reward integrates accuracy, resource efficiency, and a Useful Handoff Bonus to preserve learning continuity. Containerization further enhances adaptability. Experiments demonstrate faster convergence, improved resource utilization, and enhanced robustness under real mobility traces.	10.1109/TNSM.2026.3750310
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
Zijian Zhao, Davide Di Monda, Domenico Ciuonzo, Yipeng Wang, Antonio Pescapè	Network Intrusion Detection Adaptability under Spurious Labels: A Noise-Aware Few-Shot Class-Incremental Learning Framework	2026	Early Access	Labeling Noise Internet of Things	Network Intrusion Detection Systems (NIDSs) must incrementally adapt to emerging attacks, even when only a limited amount of labeled network traffic is available. Consequently, Few-Shot Class-Incremental Learning (FSCIL)-based NIDSs have gained growing attention. However, existing FSCIL-based NIDSs are highly vulnerable to label noise in newly-observed attack classes. To address this limitation, we propose NAFN, a noise-aware FSCIL-based NIDS framework that reduces the impact of mislabeled data during incremental adaptation. NAFN consists of two components: (i) Noise Detection, which identifies and removes mislabeled biflows using noise-detection algorithms, and (ii) Feature Sampling, which estimates class-conditional embeddings from clean biflows, calibrates them with base-class statistics, and resamples synthetic features to recover the original biflow count. The framework follows the Rethink Few-Shot (RFS) teacher–student paradigm with lightweight nearest-neighbor or logistic regression classifiers, and various pretraining & memory selection strategies. Experiments on CSE-CIC-IDS2018, Edge-IIoT, and IoT-NID datasets show that NAFN effectively preserves prior knowledge and maintains strong incremental performance even under 50% label noise, consistently improving new-class F1-scores over non-denoised baselines, up to +52%, while being time-efficient and robust in cross-network and high-noise incremental settings. The implementation is publicly available to foster reproducibility.	10.1109/TNSM.2026.3752751
Wuzhao Li, Weian Guo	Switch-Coupled Scheduling for Energy-Bounded Heterogeneous Edge AI on Commodity Ethernet	2026	Early Access	Energy Modeling Joining processes	Heterogeneous edge-AI clusters behind commodity Ethernet must jointly manage compute queues, shared links, runtime residency, and energy budgets. We formulate request admission and assignment with per-hop FCFS resources, byte-limited TTL–LRU caches, temperature-dependent service, power uncertainty, deadlines, and tenant shares. Switch-Coupled Dual Scheduling (SCD) combines deadline feasibility, cost-aware energy credits, and device plus tenant virtual debts. Analysis establishes restricted-problem hardness, a state-correct finite-horizon certificate, and guarded commitment bounds. Nominal trace-driven simulations show higher offered on-time success and lower mean response than compute-first. These gains accompany higher task energy, admitted tail latency, and mean peak age of information. The success gain over matched drift-plus-penalty scheduling is incremental. Shared link state limits queue escalation under severe simulated bottlenecks. Elastic tenant allocation restores service under tight budgets while retaining device commitment bounds. Repeated hardware experiments compare scheduling under common resource constraints, execute trained neural and tree models on heterogeneous boards, and measure Ethernet path contention. The results support competitive deadline-oriented control with explicit service and energy trade-offs.	10.1109/TNSM.2026.3752827
Jingyan Wang, Jing Bai, Lina Liu, Fumio Machida, Xiaolin Chang	Improving Dependability of VM-based Multi-Domain Service Function Chain: A Semi-Markov Modeling Approach	2026	Early Access	Aging Availability Modeling	In Network Function Virtualization (NFV) networks, service functions (SFs) can be deployed on virtual machines (VMs) across Multiple domains and then form a Service Function Chain (MSFC) for end-to-end network service provision. It is a fact of life that any software component in a VM-based MSFC must experience software aging after a long period of continuous operation. This paper quantitatively investigates the capability of proactive rejuvenation techniques in reducing the damage of software aging on a VM-based MSFC. We develop a semi-Markov model to capture the behaviors of SFs, VMs, and virtual machine monitors (VMMs) from software aging to recovery under the condition that failure times and recovery times follow general distributions. Note that the holding times of aging events follow exponential distributions. We derive the formulas for calculating the steady-state availability and reliability of the VM-based MSFC composed of multiple SFs running on VMs hosted by VMMs. Sensitivity analysis is also conducted to identify potential dependability bottlenecks. Various numerical and simulated experiments show the approximate accuracy of the proposed model and the metric formulas we derived. The experiment results also indicate that the aging time is an important parameter for VM-based MSFC dependability and the type of failure time distribution has a significant impact on dependability metrics.	10.1109/TNSM.2026.3752685
Vincent Sheh, Yao Chiang, Hung-Yu Wei	Two-Timescale Service Orchestration for Dependable Multi-Region Edge Analytics under Adversarial Workloads	2026	Early Access	Modeling Delays Routing	Adversarial workloads can saturate the shared CPU budget of edge nodes, undermining the continuous availability that safety-critical analytics require. Co-locating an inline intrusion detection system (IDS) can filter malicious traffic but creates contention with analytics inference over the same budget, producing a defense-service resource tradeoff. This tradeoff is destabilized by two mechanisms: adaptive Yo-Yo attacks that exploit container scaling delay, and load-based offloading that ignores cross-region IDS mismatch. We propose a two-timescale orchestration framework that jointly minimizes service-level objective (SLO) violations, residual malicious leakage, and benign quality degradation. A long-timescale multi-agent reinforcement learning policy partitions CPU between inspection and inference, internalizing scaling delay and coordinating across regions to suppress oscillatory reallocation. A short-timescale heuristic routes inspection traffic to receivers with compatible detection models, preventing the mismatched-IDS errors that load-based routing would induce. Compared with a representative state-of-the-art baseline, the framework achieves up to 68.8% lower SLO violation rate, 90.9% lower residual malicious leakage, and 64.6% fewer reallocations.	10.1109/TNSM.2026.3753027
Umair Ullah Tariq, Fariza Sabrina, Steven Gordon, Ahsan Morshed, Yufeng Lin, Mohammad Mamunur Rashid, Mubashir Hussain	SLA-Driven Network Planning for Hybrid QKD–PQC Security in Smart Grids	2026	Early Access	Technology Costing Seeds (agriculture)	Operational deployment of hybrid QKD–PQC security in smart grids requires coordinated planning under coupled service-level agreement (SLA) constraints, including startup latency, sustained key availability, real-time bounds, technology budget, and site storage capacity. This paper presents an SLA-driven network-planning framework solved using feasibility-first bounded local search (FF-BLS). A broad 36,300-run campaign across seven benchmark networks (14–6,495 nodes) maps deployment regimes and algorithmic ablations. Full FF-BLS attains 60.9% feasibility under fixed installed site capacity, while relaxing the site-cap constraint yields 100%. Focused studies further vary storage, technology budget, QKD-adoption target, and startup–traffic stress. FF-BLS reaches 100% feasibility at the highest tested storage multiplier (γ = 0.40), while a restrictive technology budget (b = 0.60) remains independently binding once storage is sufficient. On 246 comparison scenarios, FF-BLS achieves 100% feasibility, whereas unassisted GA, SA, Tabu, and VNS achieve 24.4–58.9%; feasibility-aware initialization and repair substantially improve the generic baselines. A separate 100-instance topology/storage stress benchmark and an aligned CPU–GPU study evaluate robustness and computational scaling. The results show that site storage is the dominant practical bottleneck under fixed installed capacity, while its importance depends on the provisioning regime. Low-to-moderate QKD adoption is most robust under constrained storage, whereas higher adoption becomes feasible when sufficient storage head-room is provisioned. These findings provide practical guidance for scalable quantum-safe smart-grid deployment under stringent SLAs.	10.1109/TNSM.2026.3751647
Diogo Pereira, Rodolfo Oliveira, Daniel Benevides Da Costa, Hyong Kim	Performance Assessment of Beamforming Training Protocols in IEEE 802.11ad/ay Networks	2026	Early Access	Modeling Probability Media Access Control	This paper introduces an innovative two-step methodology to model the beamforming training (BFT) medium access control (MAC) behavior of IEEE 802.11ad and IEEE 802.11ay networks, capturing several MAC-layer features specified in the standards that previous work has not considered. The proposed framework jointly employs two discrete-time Markov chains (DTMCs): the first models the individual behavior of each transmitting station (STA), including the dynamic adjustment of the competing STA to account for the idle period, while the second captures the network-wide interactions of all STAs and uses this information to derive the medium access probability at each frame slot during the BFT process. This work introduces two key novel modeling assumptions: (1) the STAs cease access once they successfully access a BFT frame’s slot, and (2) the number of competing STAs dynamically decreases as they enter the idle period after a given number of consecutive collisions, providing a precise representation of BFT MAC operation in IEEE 802.11ad and IEEE 802.11ay standards. We derive the theoretical expressions for the expected number of idle STAs based on probabilities of successful BFT, collisions, and transitions to the idle period.We validate the model’s accuracy through Monte Carlo simulations across diverse network scenarios, showing close alignment with simulation results and improved accuracy over prior models that do not consider these two modeling assumptions. Additionally, we analyze the impact of the collision threshold and idle period duration on BFT goodput, highlighting the model’s ability to compute MAC protocol performance.	10.1109/TNSM.2026.3751488
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
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