TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
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
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
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
Yuanming Huang, Xiaojuan Wang, Mingshu He	HyTMTC: A Pre-Training Method for Multi-Scenario Network Traffic Classification with Hybrid Transformer-Mamba	2026	Early Access		Network traffic classification is central to security monitoring and network management in heterogeneous environments. Existing deep learning approaches are often trained for a single scenario and require large amounts of labeled data, making them difficult to reuse when applications, traffic types, or encryption settings change. We present HyTMTC, a pre-training and fine-tuning framework for multi-scenario network traffic classification. HyTMTC encodes each flow as a unified multimodal token sequence that combines raw bytes, packet length, and direction, allowing protocol traces and communication behavior to be modeled together. To match the one-dimensional nature of traffic data, HyTMTC adopts a hybrid Transformer-Mamba backbone. Mamba captures contiguous byte- and packet-level patterns, while Transformer attention strengthens interactions across non-adjacent fields and packets. A self-attention fusion module further integrates the multimodal representations during fine-tuning. This design improves traffic representation without relying on scenario-specific feature engineering. Experiments on seven public datasets show that HyTMTC achieves an average F1-score of 95.03% and outperforms nine representative baselines. It also remains effective in encrypted, VPN, and few-shot settings, and maintains the ability to detect unknown attacks. These results demonstrate its effectiveness and stability for multi-scenario network traffic classification.	10.1109/TNSM.2026.3725912
Jiawei Wu, YiBo Wang, ZeLin Zhu	PPO-MS: Confidence-Aware and Collaborative Traffic Management for Multimedia Streaming in SDN	2026	Early Access		The rapid growth of multimedia streaming poses critical challenges, including bursty traffic and congestion, leading to playback delays. The existing separate prediction and control mechanisms for multimedia traffic scheduling, which are based on software-defined networks (SDN), are unable to proactively manage bursty traffic under uncertain conditions. This limitation is particularly evident in SDN-enabled backbone and multimedia-aware access networks, which typically assume centralized control and stable topologies. They lack integration of traffic prediction, traffic shaping, and real-time perception scheduling through reinforcement learning, resulting in low efficiency when exploring multiple paths in dynamic networks. To address this challenge, we propose PPO-MS (Proximal Policy Optimization-based Multimedia Scheduler), an SDN-based multimedia traffic scheduling algorithm integrating three key innovations: (1) A novel LSTM+HTB synergy where LSTM’s confidence intervals dynamically adjust HTB (Hierarchical Token Bucket) shaping parameters, enabling adaptive rate control under prediction uncertainty and overcoming the limitations of static LSTM+HTB hybrids; (2) A Deep Reinforcement Learning (DRL)-optimized path pruning method that reduces state and action spaces by generating a constrained set of k disjoint candidate paths via an improved redundant tree algorithm. Unlike traditional multi-path schemes, this method tightly couples path preselection with the RL decision loop for adaptive, context-aware routing; (3) Generalized Advantage Estimation (GAE)–accelerated PPO for stable convergence in dynamic environments. In contrast to prior works (e.g., LSTM+RL for QoE or standalone tree algorithms), PPO-MS uniquely unifies these modules through confidence-aware traffic shaping and hierarchical decision-making, validated via comparative experiments. Results demonstrate that PPO-MS, through the synergistic integration of confidence-aware traffic shaping and DRL-optimized path pruning, significantly outperforms decoupled baselines. In particular, via isolation studies against simpler alternatives (e.g., mean-prediction and fixed-margin shaping), the confidence-aware shaping mechanism is validated to be superior under bursty traffic conditions. Overall, PPO-MS reduces end-to-end latency by 17:3% and packet loss by 32:4% while achieving 24:4% better load balancing during traffic bursts.	10.1109/TNSM.2026.3725643
Chenhao Wang, Yang Ming, Hang Liu, Yutong Deng	Blockchain-Aided Authenticated Key Agreement With Message-Dependent Traceability for WBANs	2026	Early Access		As a significant building block in the intelligent healthcare system, wireless body area networks (WBANs) collect users’ real-time biomedical data, enabling application providers to provide wide medical services. Due to the open environment and wireless transmission of WBANs, the sensitive biomedical data suffers from various security threats. Although authenticated key agreement (AKA) is a promising technology to deal with these threats, the existing AKA protocols encounter deficiencies in security, privacy, and practicality. Therefore, in this paper, we propose a blockchain-aided authenticated key agreement protocol with message-dependent traceability (BAKA-MT) for WBANs. In the proposed BAKA-MT, the user device can establish one or more different session keys with application providers to guarantee security of the transmitted biomedical data. Meanwhile, the manager is able to monitor network status by revealing the real identity of malicious entities that release illegal messages. Conversely, the identity privacy of honest entities transmitting legitimate data is still protected. In addition, blockchain with smart contract is adopted in BAKA-MT to ensure mutual authentication between communication entities and revoke malicious entities. Finally, the rigorous security proof and performance evaluation demonstrate that BAKA-MT is secure and practical.	10.1109/TNSM.2026.3725642
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
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
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
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
Martine S. Lenders, Carsten Bormann, Thomas C. Schmidt, Matthias Wählisch	A Leaner and Faster Web: How CBOR Can Improve Dynamic Content Encoding in JSON and DNS over HTTPS	2026	Early Access	Internet of Things Encoding Internet	The Internet community has taken major efforts to decrease latency on the World Wide Web with significant improvements in accelerating content transport and in compressing static content. Less attention, however, has been dedicated to compression of dynamic content. Such content is commonly provided by JSON and DNS over HTTPS. Dynamic content objects continue to grow in size, which increases latency and fosters the digital inequality. In this paper, we propose to mitigate this increase by utilizing Concise Binary Object Representation (CBOR), a standard originally designed for the constrained Internet of Things (IoT) to restrict packet sizes and enable efficient encoding of data objects. We provide protocol design and three new data sets for the evaluation of dynamic content, DNS, and the loading of websites. Our key findings are the following: (i) Switching the data representation from JSON to CBOR reduces data by up to 80%. This size reduction can decrease loading times by up to 13.8% when downloading large objects—even in local setups. (ii) Enabling CBOR for DNS over HTTPS (DoH) and DNS over CoAP (DoC) reduces packet sizes significantly. Compressing only names combined with unpacked CBOR achieves maximum gain of 52.2%, using more complex but still lightweight Packed CBOR allows minimizing packets by up to 95.5%. Our lean decoder for name compression can fit into as little as 314 bytes of build size. Our results clearly show the potential of CBOR outside of IoT scenarios. Parts of this research have already influenced work within the IETF.	10.1109/TNSM.2026.3722114
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
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
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
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
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
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
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
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
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