TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Hayla Nahom Abishu, Ahmed Badawy, Amr Mohamed, Carla Fabiana Chiasserini	Reliability and Traffic Aware Resource Allocation for UAV-assisted Vehicular O-RAN	2026	Early Access	Massive machine type communications Resource management Autonomous aerial vehicles	The rapid advancements of next-generation vehicular networks require intelligent, low-latency, and efficient resource management to support heterogeneous services. In this work, we propose a Traffic-aware Dynamic Resource Allocation (TADRA) architecture for UAV-assisted vehicular O-RAN to address the challenges of dynamic traffic conditions, infrastructure failures, and stringent quality of service (QoS) requirements. Due to the dynamic mobility and flexible deployment characteristics, UAV Open Radio Units (O-RUs) in the TADRA architecture support the terrestrial infrastructure under overload or failure conditions, dynamically extending coverage, balancing traffic loads, and restoring service to maintain uninterrupted QoS across diverse and heterogeneous traffic demands. Unlike existing static or single-layer solutions, our proposed TADRA integrates RAN Intelligent Controllers (RICs) with a Hierarchical Traffic-Aware Multi-Agent Twin-Delayed (TMT) algorithm to optimize the allocation of computation and radio resources. This joint optimization problem is NP-hard, highly dynamic, and coupled across agents, making TMT a tractable and adaptive alternative. This hierarchical framework performs traffic prioritization at the upper (application) layer and resource allocation at the lower (MAC) layer, facilitating adaptive decision-making under diverse vehicular traffic patterns. Numerical results demonstrate that our solution provides substantial gains over MATD3, MADDPG, and GA, achieving 17% lower latency, 10% higher throughput, 14% lower energy consumption, and 6.5% higher reliability.	10.1109/TNSM.2026.3722320
Nguyen Quang Hieu, Dinh Thai Hoang, Diep N. Nguyen, Mohammad Abu Alsheikh, Carlos C. N. Kuhn, Yibeltal F. Alem, Ibrahim Radwan	End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systems	2026	Early Access	Receivers OFDM Quantization (signal)	Full 3D human pose reconstruction is a critical enabler for extended reality (XR) applications in future sixth generation (6G) networks, supporting immersive interactions in gaming, virtual meetings, and remote collaboration. However, achieving accurate pose reconstruction over wireless networks remains challenging due to channel impairments, bit errors, and quantization effects. Existing approaches often assume error-free transmission in indoor settings, limiting their applicability to real-world scenarios. To address these challenges, we propose a novel deep learning-based framework for human pose reconstruction over orthogonal frequency-division multiplexing (OFDM) systems. The framework introduces a two-stage deep learning receiver: the first stage jointly estimates the wireless channel and decodes OFDM symbols, and the second stage maps the received sensor signals to full 3D body poses. Simulation results demonstrate that the proposed neural receiver reduces bit error rate (BER), thus gaining a 5 dB gap at 10−4 BER, compared to the baseline method that employs separate signal detection steps, i.e., least squares channel estimation and linear minimum mean square error equalization. Additionally, our empirical findings show that 8-bit quantization is sufficient for accurate pose reconstruction, achieving a mean squared error of 5×10−4 for reconstructed sensor signals, and reducing joint angular error by 40% for the reconstructed human poses compared to the baseline. The practical feasibility of our framework is demonstrated through validation in an open-source ray tracing simulator incorporating realistic 3D scenes and human models.	10.1109/TNSM.2026.3722545
Akhilesh Patel, Yatindra Nath Singh	Scalability and Delay Analysis of XR Traffic in Optical Access Networks	2026	Early Access		The rapid evolution of 5G and emerging 6G networks requires optical access systems to support immersive extended reality (XR) services with stringent quality-of-service (QoS) requirements, like ultra-low latency and high bandwidth. However, conventional dynamic bandwidth allocation (DBA) schemes in passive optical networks (PONs) allocate upstream bandwidth solely based on reported queue occupancy, without considering the unique characteristics of XR traffic. To address these limitations, we propose an XR-aware Predictive (XP)-DBA scheme that integrates XR traffic prediction, deadline-aware scheduling, adaptive grant control, and a cycle-controller to proactively allocate bandwidth, prioritize latency-critical packets, and limit polling-cycle growth. We also derive closed-form analytical expressions to characterize XR-specific stability and delay feasibility in PON systems. We evaluate XP-DBA under standardized and burst-enhanced XR traffic models across varying XR user densities and transmission distances of up to 100 km. The results show that XP-DBA will reduce latency, jitter, and polling-cycle time while increasing throughput and supporting higher XR user densities under heavy network loads without violating XR delay bounds. These findings establish XP-DBA as an efficient and scalable scheduling solution for next-generation immersive XR services over long-reach optical access networks.	10.1109/TNSM.2026.3725139
Martin Trullenque Ortiz, Daniel Camps Mur, Oriol Sallent, Jad Nasreddine	FOM-5G: A Learning-Based Framework for Overload Mitigation in V2X-Enabled 5G Networks	2026	Early Access		Public networks deploying 5G-Advanced technology are expected to support large-scale connected and cooperative vehicular services. To this end, 3GPP has enabled the deployment of Day 1 V2X safety services over public cellular networks, which provide the global coverage and low latencies these services require. However, high vehicular mobility may result in traffic jams that strain radio resources, leading to cell overload situations that jeopardize the delivery of V2X services. Previous studies have shown that the best action to mitigate cell overloads strongly depends on road topology and the location of the traffic jam with respect to the cell geometry. Moreover, a fundamental insight to address V2X related cell overloads is to realize that the majority of traffic jams are repeatable, hence historical information should be used to take radio resource management (RRM) decisions. In this paper, we present a novel framework for cell overload mitigation in 5G networks (FOM-5G), an RRM solution for ORAN-based public 5G networks that leverages historical information to prevent recurring cell overloads, particularly those affecting V2X services. We validated FOM-5G through comprehensive simulations incorporating three real vehicular mobility patterns from a European city experiencing traffic congestion. The results show that FOM-5G can adapt its congestion control actions to varying traffic conditions, effectively exploit historical data, and achieve superior performance compared to static congestion mitigation approaches.	10.1109/TNSM.2026.3725207
Qiru Chen, Xinping Guan, Lei Xu, Yanzhou Zhang, Qimin Xu, Cailian Chen	Knowledge-Aware Schedulability Analysis for Time-Sensitive Networking: A GNN-Based Method	2026	Early Access		Industrial automation is rapidly evolving toward flexible production. This transition requires networks to ensure the deterministic transmission of varying traffic sets across different production stages. Consequently, the system must be capable of rapidly analyzing whether fixed network resources can accommodate all service requirements prior to actual scheduling. While Time-Sensitive Networking (TSN) provides the deterministic transmission for such environments, existing schedulability assessments rely on exhaustive scheduling tests. However, the scheduling process is inherently an NP-hard constraint satisfaction problem, whose heavy computational overhead severely limits deployment agility. Therefore, it is critical to develop a method that can rapidly predict the constraint satisfiability of diverse traffic sets without repetitive and time-consuming scheduling. In this work, given the inherent graph-structured nature of network infrastructure and traffic patterns, we design a graph neural network model to explicitly capture complex spatial dependencies. As node attributes, sparse basic traffic features are distilled as expert knowledge and integrated, thereby enhancing prediction accuracy. When a traffic set is deemed unschedulable, we explore the traffic features and links with the greatest impact. Based on this, a feature-driven rerouting strategy is proposed to find a more schedulable traffic behavior. The evaluation results show that the model demonstrates the capability to process thousands of datasets within hundreds of microseconds, while guaranteeing a prediction accuracy of over 90% and an increase in the count in schedulable flows by about 25% compared to the standard Dijkstra’s shortest path algorithm baseline.	10.1109/TNSM.2026.3724835
Yuyu Zhao, Siyuan Zhou, Guang Cheng, Yuyang Zhou, Zihan Chen, Wei Zhang	CPRANT: Towards a Computing Power Network Telemetry Architecture Based on Collaborative SRv6 and FPGA Optimization	2026	Early Access	Telemetry Planing Field programmable gate arrays	The emergence of Computing Power Networks (CPN) as critical AI infrastructure poses a key networking challenge: how to obtain visibility across the network into computing and transport resources in real time while minimizing the telemetry overhead on user traffic and control/forwarding devices. This paper introduces CPRANT (Computing Power Networks Telemetry based on SRv6 and FPGA), an FPGA-based plug and play In-Band Network Telemetry (INT) framework that addresses this challenge through a joint hardware and software design. The core innovation lies in using FPGA spatial parallelism to establish physically isolated processing paths for telemetry tasks and forwarding tasks, achieving line rate packet processing with a verified CPN’s forwarding latency of only 15 ns. We propose a novel INT mechanism based on SRv6 that eliminates linear metadata accumulation at each hop in traditional INT via SID reconstruction and time division multiplexing, reusing native SRv6 header space and preserving forwarding compatibility. CPRANT further incorporates adaptive sampling and redundant path elimination algorithms to dynamically optimize telemetry processes, demonstrating a 52.4% reduction in bandwidth and a 45.9% reduction in the load of the control plane. Experimental validation confirms the operational efficiency of CPRANT, maintaining high information fidelity (effective telemetry information yield > 0.85) and comprehensive coverage (path overlap < 4%) with a lightweight resource footprint (< 60 MB). This solution provides a scalable telemetry paradigm for mission critical CPN applications, particularly in large scale AI deployment scenarios requiring monitoring that does not disrupt services.	10.1109/TNSM.2026.3722680
Xili Wan, Fuliang Yang, Xinjie Guan, Zuwei Zhang, Yihan Ji	Joint Dataflow and In-Network Computing Resource Optimization for Dynamic LEO Satellite Systems	2026	Early Access	Satellites Information rates Throughput	Low Earth Orbit (LEO) satellite constellations are emerging as an important platform for distributed dataflow execution in space-terrestrial integrated networks. Existing studies largely treat routing and processing separately, while next-generation LEO systems are expected to process and transform data in transit by leveraging on-board computing and software-defined infrastructures. However, jointly optimizing routing and in-network processing in dynamic LEO satellite networks remains challenging because of time-varying connectivity, limited on-board resources, and bandwidth constraints. In this paper, we formulate the Dynamic LEO In-network Processing Dataflow Optimization (DLIDO) problem, which aims to maximize the throughput of processed dataflows by jointly optimizing routing paths and processing-resource allocation over a dynamic flow network. We present an approximation algorithm with a proven (1−ϵ) approximation guarantee for 0 < ϵ ≤ 0.5, providing near-optimal throughput under dynamic processing and communication constraints. To further improve efficiency and practicality, we develop a 2-walk based iterative heuristic algorithm that substantially reduces runtime while maintaining strong empirical performance, and in some regimes provably optimal behavior. Extensive evaluations on realistic LEO network topologies show that both algorithms significantly outperform existing approaches in throughput and adaptability, highlighting a promising direction for dataflow-aware scheduling and optimization in dynamic satellite systems.	10.1109/TNSM.2026.3722902
Didik Sudyana, Wong Yu Xuan, Laurens D’hooge, Ren-Hung Hwang, Narn-Yih Lee, Pei-Yin Chen, Tim Wauters, Bruno Volckaert, Filip De Turck	Bridging Training–Deployment Gap in Intrusion Detection with Source-Free Domain Adaptation	2026	Early Access	Modeling Internet of Things Fluid flow	Machine learning (ML)–based intrusion detection systems (IDS) frequently degrade when deployed across heterogeneous networks due to domain shifts in traffic composition and monitoring configurations. Conventional domain adaptation (DA) methods mitigate this issue by aligning source and target distributions, but they often rely on retaining source-domain data at deployment—an impractical requirement that undermines operational scalability and reusability. To address this gap, we propose TRANSFA-IDS (Transformer Source-Free Adaptation for IDS), a lightweight source-free adaptation framework that recalibrates a source-trained IDS using only target traffic data. TRANSFA-IDS converts tabular flow records into structured RGB image embeddings and employs a compact Vision Transformer with a Deep Support Vector Data Description (Deep-SVDD) head to learn transferable normal representations. At deployment, adaptation is performed by fine-tuning only the last transformer block on a small target buffer, realigning target representations without retraining or access to source data. Experiments on cross-dataset transfer between CIC-IDS-2018 and UNSW-NB15 show that TRANSFA-IDS achieves AUROC of 0.9177 and 0.9071 in the two transfer directions, reduces target-domain benign false positives by over 60% relative to the same source-pretrained model deployed without source-free adaptation, and adapts substantially faster than supervised and unsupervised DA baselines while using at most 20% of the target-domain data. These results indicate that source-free adaptation can achieve both strong detection performance and a practical deployment-oriented design, with cross-benchmark evidence of scalable adaptation across heterogeneous network environments.	10.1109/TNSM.2026.3723866
Ping He, Yu Yao, Xu Li, Yao Hu, Wei Yang	FIGAN: Diversity-Oriented Traffic Generation for Industrial Protocol Format Inference	2026	Early Access	Protocols Modeling Fuzzing	Protocol Format Inference is a pivotal step in the reverse engineering of proprietary protocols, yet its effectiveness is constrained by the scarcity of high-quality training data. In industrial control systems, the rigid and cyclical nature of traffic results in a "long-tail" distribution, where diverse functional scenarios are severely underrepresented. Existing generative approaches, primarily designed for fuzzing or intrusion detection, fail to resolve the intrinsic conflict between syntactic validity and semantic diversity required for protocol format inference. To bridge this gap, we propose FIGAN, a stage-wise decoupled generative framework tailored to synthesize high-fidelity traffic for protocol format inference. By isolating flexible distribution learning from rigid syntax enforcement, FIGAN liberates the generative process to extrapolate novel payload variations from a continuous latent space, effectively surmounting the limitations of sparse seed data. Specifically, the framework integrates three synergistic modules: first, heuristic pre-processing that constructs semantic templates as a prior knowledge base; second, a generative adversarial architecture optimized via discrete relaxation to explore high-dimensional payload patterns independently of syntax rules; and finally, a closed-loop verification mechanism that performs syntactic calibration and functional validation against simulated device responses. Evaluations on four real-world protocols (Modbus TCP, S7Comm, Omron FINS, and DNP3) demonstrate that FIGAN significantly outperforms state-of-the-art baselines. The source code has been open-sourced https://github.com/MissHP111/FIGAN.	10.1109/TNSM.2026.3717268
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
Depeng Xu, Guozhen Cheng, Hongchao Hu, Quan Ren, Xiaohan Yang, Kangxu Wang	STNet: Multi-Scale Spatiotemporal Learning and Adaptive Fusion for Few-Shot Tor Traffic Classification	2026	Early Access	Modules (abstract algebra) Modeling Accuracy	The Tor network’s anonymity is increasingly exploited for cybercrime, creating a demand for accurate traffic classification under strict few-shot constraints. While recent efforts like WF-Transformer demonstrate strong temporal modeling capabilities, they still require abundant labeled data and struggle to generalize under defense-induced distortions and open-world unknown traffic. To address these gaps, we propose STNet (SpatioTemporal Multi-scale Augmentation and fusion Network), an episode-based few-shot learning architecture for Tor traffic classification. Unlike simple module stacking, STNet adopts a modular decoupling design: (1) a Multi-Scale Spatiotemporal Feature Fusion (MSMF) module captures packet-level and flow-level patterns to resist obfuscation; (2) scenario-adaptive modules tackle domain shifts in closed-world settings and feature scarcity in open-world settings; and (3) a Hierarchical Layer Attention (HLA) mechanism dynamically fuses heterogeneous features from different deployment positions. Extensive experiments on real-world Tor traffic show that STNet consistently outperforms representative baselines including WF-Transformer. In closed-world settings, it limits the accuracy drop under WalkieTalkie obfuscation to 13.6 percentage points. In open-world 10-shot evaluation, it achieves 92.1% AU-COVR and 79.1% unknown-class F1-score, surpassing the best baseline by 4.9 and 6.0 percentage points, respectively. These results demonstrate the effectiveness of decoupling universal feature extraction from scenario-specific adaptation in few-shot Tor traffic analysis.	10.1109/TNSM.2026.3722541
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
Wei Sai, Yihui Lu, Xin Guo	A Privacy-Preserving Security Framework for Multi-Party Data Fusion Computing Based on Homomorphic Encryption	2026	Early Access	Security Protocols Information rates	To prevent plaintext exposure in multi-party collaborative computing, this paper proposes a distributed secure multi-party computation protocol based on the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme. Data is encoded and encrypted at the source into CKKS complex polynomial ciphertext, enabling vectorized fusion under shared evaluation keys and threshold decryption in a decentralized architecture without a trusted central authority. Experiments on heterogeneous multi-institution datasets demonstrate low numerical error (9.0×10⁻⁷ at polynomial order 2¹⁶ and depth 12), effective scalability (throughput increasing from 1.12×10⁵ to 1.32×10⁵ ops/s and latency decreasing from 56 ms to 38 ms as nodes scale from 4 to 16), and strong robustness (70% decryption success at a 60% threshold and 95% recovery under malicious interference), showing that the framework achieves efficient computation with strict privacy protection for cross-party data fusion.	10.1109/TNSM.2026.3717343
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
Franck Messaoudi, Luhan Wang, Abdelkader Mekrache, Adlen Ksentini, Bingxuan Li, Jialei Su, Sofiane Messaoudi, Salim El Ghalbzouri	The Brewing Storm in 5G’s Data Plane: Design and Evaluation of a High-Performance eBPF/XDP-Based User Plane Function	2026	Early Access	Quality of service Fluid flow Kernel	This paper presents the design and implementation of a novel 5G UPF leveraging eBPF technology to meet the stringent performance and programmability requirements of emerging 6G systems. Traditional UPF implementations often struggle to balance performance, flexibility, and resource efficiency-challenges particularly critical in CPU- and I/O-constrained edge environments. The proposed eBPF-based UPF architecture mitigates these limitations by embedding core functionalities, such as packet classification, forwarding, and QoS enforcement, directly within the Linux kernel via eBPF programs attached through XDP and tc hook points. Performance evaluation using TRex demonstrates that the proposed solution achieves competitive throughput, low packet loss, and efficient CPU utilization across traffic profiles. Moreover, it maintains full compliance with 5G Core Network standards. Comparative analysis with well-established open-source UPF implementations further underscores its advantages. This work highlights the potential of eBPF as a foundational technology for building next-generation, programmable UPFs optimized for edge cloud deployments in the 6G era.	10.1109/TNSM.2026.3720812
Francisco Muro, Eduardo Baena, Tomaso De Cola, Sergio Fortes, Raquel Barco	AI-Driven Optimization of Virtual Network Function Allocation in 6G Non-Terrestrial Networks	2026	Early Access	Resource management Optimization Satellites	The integration of 6G technologies into Non-Terrestrial Networks (NTNs) raises a fundamental orchestration problem: how to allocate Virtual Network Functions (VNFs) across satellite and terrestrial domains under tight onboard resource constraints and a continuously changing topology. The virtualized 6G Open Radio Access Network (O-RAN) paradigm makes it possible to run 5G software stacks on Software-Defined Radios (SDRs) based on General Purpose Processors (GPPs), but it also turns VNF placement into a high-dimensional, multi-objective decision that static heuristics and model-based formulations struggle to capture. This paper addresses that gap by introducing an AI-driven VNF allocation framework for 6G-NTN environments built on an O-RAN-based distributed architecture and orchestrated on top of Kubernetes. The VNF allocation problem is formalized for a multi-domain 6G-NTN scenario with constrained satellite resources, and a measurement-based test campaign is designed to characterize the emulated platform in terms of virtual resource utilization and end-to-end performance. The framework couples tree-based machine learning predictors with a gradient-free optimizer to reach the optimal feasible allocation, outperforming two heuristic baselines drawn from the VNF placement literature by reducing the service RTT by up to 39% and delivering up to 3× higher YouTube DL throughput with respect to the best feasible heuristic. Beyond these gains, the proposed framework establishes a measurement-driven, reproducible methodology for VNF allocation in 6GNTN scenarios, demonstrating that AI-driven orchestration can systematically uncover non-obvious resource configurations that purely analytical or static approaches consistently miss.	10.1109/TNSM.2026.3724474
Nan Wei, Sizhe Huang, Lihua Yin, Ziying Zhu, Wenting Wang	Correction Forest: A Misclassification Correction model for Reducing the Total Error Rate of IIoT Network Intrusion Detection	2026	Early Access	Modeling Industrial Internet of Things Internet of Things	Deep learning-based network intrusion detection systems (NIDSs) in the Industrial Internet of things (IIoT) are inevitably prone to producing misclassified samples. Correction models can identify and correct these samples to reduce the total error rate (TER) of NIDSs. Existing correction models fail to account for the uneven distribution of misclassified samples in the prediction intervals of NIDSs due to IIoT data imbalance, re-sulting in over-correction and increased TERs. Given this, we propose a novel correction model called Correction Forest for correcting the misclassified samples of NIDSs targeting imbalanced IIoT network traffic dataset. Correction Forest adopts a generation-correction strategy. The generation process divides the output values of NIDSs with imbalanced dataset into fine-grained bins, and then generates novel misclassification features for each bin using a balanced hybrid Random Forest. The correction process calculates feature importance score for misclassification features and corrects misclassification samples by a K-Nearest Neighbors (KNN) -based algorithm. Evaluated on 15 imbalanced IIoT datasets with varying malicious sample ratios, Correction Forest significantly outperforms 4 state-of-the-art models. Under the 1.25% setting of NSL-KDD, Correction Forest improves F1-Score from 0.3144 to 0.7345, an absolute gain of 0.4201. On the TON_IoT at 12.5%, it achieves a maximum R.TER of 0.3750 among the state-of-the-art models.	10.1109/TNSM.2026.3724323
Elham Amini, Jelena Mišić, Vojislav B. Mišić	Deadline-Aware SRPT Scheduling for Paxos Consensus	2026	Early Access	Schedules Scheduling Timing	Paxos consensus protocol is widely used in distributed systems, yet their performance can degrade under heterogeneous workloads and deadline-constrained requests. Traditional priority-based Paxos extensions rely on static scheduling policies that are unable to adapt to dynamically changing urgency. This paper proposes a deadline-aware scheduling framework for Paxos based on the Shortest Remaining Processing Time (SRPT) discipline. The proposed approach dynamically prioritizes requests according to their expected completion behavior and their likelihood of meeting assigned deadlines, enabling preemptive scheduling decisions at the consensus leader. An analytical queueing model is developed to characterize the mean waiting time, mean residence time, and mean response time under SRPT scheduling. Using these results, a probabilistic measure of deadline satisfaction is derived and employed to guide scheduling decisions. Analytical and numerical evaluations demonstrate that the proposed framework significantly improves deadline satisfaction while preserving the delay-optimal properties of SRPT, making it well suited for latency-sensitive Paxos deployments.	10.1109/TNSM.2026.3724325
Yidi Zhang, Lanlan Rui, Sining Wang, Jie Zhang, Zhipeng Gao, Xuesong Qiu, Shaoyong Guo	NEKC: Few-shot Anomaly Knowledge Completion for Intelligent Network Management	2026	Early Access	Modeling Knowledge graphs Anomaly detection	Intelligent network operations increasingly rely on structured anomaly knowledge to support anomaly analysis, alert correlation, and root-cause investigation. However, under emerging and few-shot anomaly scenarios, anomaly-related knowledge graphs are often incomplete, which limits their operational value. To address this issue, this paper studies the problem of few-shot anomaly knowledge completion and proposes a Neighbor-Enhanced Knowledge Graph Completion model (NEKC). NEKC employs a similarity-aware neighbor selection mechanism to retain semantically relevant neighbors while introducing diversity constraints to avoid representation bias caused by overly homogeneous neighborhoods. An attention mechanism is further used to dynamically weight neighbor entities, and a Transformer-based encoder is adopted to capture contextual dependencies for task-specific relation representation. To evaluate the proposed method, experiments are conducted on the generic few-shot knowledge graph completion benchmark NELL and on a constructed Network Anomaly Knowledge Graph (NAKG) derived from public anomaly-related knowledge sources. The results show that NEKC achieves moderate overall improvements on NELL, whereas its advantages are more evident on NAKG compared with representative baseline methods in few-shot link prediction and knowledge completion tasks. These results indicate that NEKC can effectively improve the completeness of anomaly knowledge graphs and provide semantic support for downstream anomaly analysis in intelligent network operations.	10.1109/TNSM.2026.3724317
Amin Bashiri, Majid Khabbazian	Resilient Onion Messaging in the Lightning Network	2026	Early Access	Probability Joining processes Algorithms	Onion messages (OMs) in the Lightning Network (LN) enable private communication between nodes through onion routing. While they support important functionalities such as static invoices and asynchronous payments, they can also be exploited for spam. To counter this, the Basis of Lightning Technology Specifications (BOLTs) recommend implementing rate limiting on OM forwarding. Limiting the rate, however, opens the door for an adversary to degrade the OM service by flooding the network—anonymously, thanks to onion routing. In this paper, we analyze and quantify the impact of DoS attacks on the OM service. Following the approach suggested in the literature, we study a policy in which per-peer forwarding limits and forwarding-node selection probabilities are proportional to publicly observable channel-capacity weights. Our analysis shows that OMs are most resilient against DoS attacks when the honest nodes’ public-capacity weights are uniformly distributed. However, the distribution of these weights in LN is highly non-uniform, as demonstrated in prior empirical studies and confirmed by our analysis of three network snapshots spanning 2022–2026. To improve resilience under such skewed conditions, we propose restricting forwarding-node selection to a carefully selected subset of nodes. For the evaluated setting l = L = 3, at a common adversarial public-capacity weight of approximately 14.30 BTC—equivalent to about USD 1 million at the reference BTC/USD rate used in our evaluation—the top-target per-connection adversary matches the strong optimal aggregate-budget adversary in each snapshot. Their OM failure probabilities range from 40.9% to 56.7% when the full network is available for forwarding-node selection. Restricting selection to the optimized subsets reduces these probabilities to 3.6%–3.9%, with corresponding analytical upper bounds of 6.2%–6.9%.	10.1109/TNSM.2026.3724114