TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Tao Zhang	How to select blockchain applications for covert communication	2026	Early Access	Bitcoin Privacy Receivers	With characteristics like decentralization, transparency, anonymity and security, blockchain is an ideal platform for covert communication. How to select suitable blockchain platform is important to achieve secure and efficient covert communication with blockchain in practical scenarios. However, there is no systematical evaluation metrics currently. In the paper, we propose the first evaluation system with 10 key evaluation metrics from identity privacy, information stealthiness, robustness, performance and cost to systematically evaluate blockchain based covert communication schemes. Then different blockchain applications are compared with these metrics, comparative analysis shows that these blockchain applications have both advantages and disadvantages for covert communication. In the future, blockchain applications with improved privacy protection, lower latency and transaction fee can be explored in covert communication scenarios. Our work can lay the foundation and provide guidelines to assess blockchain based covert communication schemes and select blockchain applications suitable for covert communication.	10.1109/TNSM.2026.3742609
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
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
Hongyuan Cheng, Lihua Yin, Tianqing Zhu, Hongyu Yang, Dexin Zhu, Weixiang Jiang, Nan Wei	Pattern2Bot: A malicious server IP detection model based on self-trained heterogeneous graph neural network	2026	Early Access	Labeling Modeling Signal detection	Botnets play a key role in network attacks such as Distributed Denial-of-Service (DDoS) attacks, spamming and click fraud. Command & Control server is an important node for botmaster to control bots and distribute attack commands. Bot disguise as normal domain requests through DNS traffic, but leave malicious records in DNS logs containing client IP, query domain, server IP and other complex interactions. This information provides important clues for detecting those botnets. In the current study, researchers detect botnets through multiple perspectives, including abstracting DNS communication records into graphs and using graph neural network models for detection. Conventional approaches often necessitate massive labeled datasets for model training, which substantially incurs training costs. Moreover, despite enriching node embeddings via topological relations, graph models often suffer performance degradation when confronted with structural semantic obfuscation from evasion techniques like double-flux. In order to understand the abstract and complex relationship patterns in DNS records while reducing label usage to lower training costs, we propose a botnet Command & Control server detection model based on DNS traffic. This is a self-training heterogeneous graph neural network model that does not require a large number of labels. By integrating the high-order relational semantics derived from multiple meta-paths with the local topologies of target nodes, the model achieves finer-grained structural awareness and semantic abstraction, thereby enhancing detection performance. We validate the effectiveness of the model in a real dataset, and the results show that the model has a better understanding of the special structure possessed by the IPs of Command & Control servers.	10.1109/TNSM.2026.3741111
Aymen Daoud, Md. Zoheb Hassan, Georges Kaddoum	SpectraTwin: Generative Intelligence-Powered Digital Twin for Dynamic Spectrum Awareness	2026	Early Access	Modeling Radio frequency Labeling	Spectrum monitoring and classification under evolving RF spectrum landscapes are key enablers of spectrum awareness for dynamic spectrum management and interference detection in 6G networks. Although artificial intelligence (AI) and machine learning (ML) techniques have shown promising performance in spectrum classification, most existing models are static, non-generative, and optimized for specific training environments. As a result, channel distribution shifts, model brittleness, and the scarcity of labeled RF data for fine-tuning AI/ML models lead to significant performance degradation in evolving RF spectrum landscapes. In this context, digital twin (DT) provides a promising approach by maintaining a virtual representation of the RF spectrum environment that supports what-if analysis, data generation, and iterative model updates. In this paper, we propose SpectraTwin, a multi-AI-enabled, adaptive, and self-evolving DT framework for multi-timescale spectrum classification under evolving RF spectrum states and incrementally increasing task complexity. SpectraTwin integrates three tightly coupled modules: (i) a Dynamic Mask Generative Adversarial Network (GAN) that serves as the virtual replica of the RF spectrum environment, synthesizing realistic, classand channel-conditioned spectrograms on demand to mitigate RF data scarcity; (ii) a continual learning-based spectrum classification model that incrementally adapts to evolving RF environments while preserving prior knowledge; and (iii) an active-learning-inspired orchestration module that decides when to adapt, which classes to synthesize, and which exemplars to retain, so that model updates are performed in response to detected change rather than on a fixed retraining schedule. By jointly combining generative, continual, and active learning, SpectraTwin enables robust spectrum awareness through (a) faithful emulation of RF spectrum evolution, (b) detection of domain distribution shifts, and (c) efficient incremental adaptation under non-stationary conditions while maintaining a favorable balance between accuracy and forgetting. Simulation results on both custom dynamic RF datasets and multiple benchmark datasets (consisting of simulated and real-world signal captures) show that SpectraTwin achieves superior adaptability and classification performance compared with existing approaches.	10.1109/TNSM.2026.3743810
Nazmul Islam, Mohammad Zulkernine	CONTEX-T: Contextual Exploitation of Encrypted Traffic for Device Fingerprinting via Transformer Time-Frequency Analysis	2026	Early Access	Modeling Internet of Things Frequency	The rapid expansion of internet of things (IoT) devices has created a pervasive ecosystem where encrypted wireless communications serve as the primary privacy and security protection mechanism. While encryption effectively protects message content, contextual information from packet metadata and statistics inadvertently expose device identities. Various studies have exploited raw packet statistics and their visual representations for device fingerprinting and identification. However, these approaches remain confined to the spatial domain with limited feature representation. Therefore, this paper presents CONTEX-T, a novel framework that exploits device-level information from encrypted traffic metadata using temporal and spectral representation. CONTEX-T first transforms raw packet-length sequences into temporal and spectral representations and then utilizes vision transformers (ViTs) for device identification. We systematically evaluated multiple time-frequency (TF) decomposition techniques and transformer-based models across two heterogeneous, encrypted IoT traffic datasets. The experiments demonstrated that TF analysis provides new and rich feature representation, exposing device-discriminative structure that remains latent in spatial-domain representation. It also demonstrated that temporal-spectral signatures persist regardless of payload encryption, highlighting packet-length metadata as an exploitable side channel for IoT device fingerprinting.	10.1109/TNSM.2026.3743753
Di Wu, Jian Xu	LLM-LogHGC: Hypergraph Enhanced Unsupervised Log Parsing with Lightweight LLMs	2026	Early Access	Large language models Modeling Accuracy	Log parsing converts log messages into structured data, serving as a critical step in log analysis. Traditional log parsers rely on predefined rules or on trainable models that require labeled data. Although they are effective in particular settings, they exhibit limited generalizability and struggle to handle complex log data. Recent advances in large language models (LLMs) have enabled LLM-based log parsers to achieve outstanding performance. However, existing LLM-based parsers exhibit limited sensitivity to structural information, and pose privacy risks when using commercial models. Log data naturally exhibits pronounced structural patterns, and its intrinsic regularities implicitly encode log template information, enabling lightweight LLMs to parse it accurately. In this paper, we propose LLM-LogHGC, a log parsing framework enhanced with hypergraph structures, deployable on lightweight open-source LLMs. Without requiring manually labeled logs for contextual learning or fine-tuning, it leverages the hypergraph structure to capture latent relationships and similarities between logs to assist parsing. Specifically, LLM-LogHGC models each log message as a hyperedge, with the tokens in the message serving as nodes, thereby constructing the log hypergraph. Logs are preliminarily partitioned based on hyperedge connectivity. Subsequently, LLM-LogHGC uses a specially designed Masked Attention Hypergraph Convolutional Network to learn hyperedge embeddings and compute their similarities. Finally, similar log messages are input into the LLM for parsing. We introduce a template cache to reduce LLM queries and a structural verification mechanism to mitigate LLM-induced errors, improving parsing efficiency and accuracy. Comprehensive experiments on public datasets demonstrate that LLM-LogHGC outperforms state-of-the-art methods.	10.1109/TNSM.2026.3743629
Messaoud Ait-Yahia, Wael Jaafar, Rami Langar	Joint Design of Blockchain-Enabled Service Placement and Task Assignment in Vehicular Fog Computing Networks	2026	Early Access	Delays Timing Optimization	Driven by the evolution of blockchain and fog computing, vehicular networks are increasingly capable of supporting latency-sensitive applications with enhanced security and trust guarantees. However, the joint resource allocation for task offloading and blockchain services has been insufficiently investigated in existing works. To address this gap, this paper proposes a framework for jointly allocating resources of blockchain, users’ virtualized services, and Mobile Edge Computing (MEC) task assignment in Vehicular Fog Computing (VFC) networks. Specifically, we formulate the optimization problem as an integer nonlinear programming model aiming to maximize the satisfaction rate of users’ service requests while minimizing the corresponding blockchain operation time under mobility, queuing, instantiation, and resource constraints. To solve it in a timely manner, we design two-stage hierarchical low-complexity solutions, namely a Particle Swarm Optimization-based Joint Blockchain-enabled Service placement and Task Assignment algorithm (PSO-JBSTA), and a Genetic Algorithm-based approach (GA-JBSTA). Through extensive simulations, we demonstrate the effectiveness of PSO-JBSTA (resp. GA-JBSTA) and their adaptability to network conditions, achieving an average 35% (resp. 24%) improvement in users’ service satisfaction rate and 9.5% (resp. 10.2%) reduction in average blockchain validation delay compared with the baselines.	10.1109/TNSM.2026.3737068
Ara Cho, Yeram Kang, Hyesook Lim	A New Flow Table Update Method Using Invertible Bloom Filters in SDN-IoT and SDN-IoV Environments	2026	Early Access	Software defined networking Fluid flow Switches	Reducing the communication overhead associated with flow table updates is important for software-defined networking (SDN) in dynamic Internet of Things (IoT) and Internet of Vehicles (IoV) environments, where a large number of flow entries must be installed to accommodate diverse devices and traffic flows. However, the OpenFlow protocol updates resulting in considerable communication overhead between the controller and the switches when a large number of flow entries are added or modified. To address this limitation, we propose an invertible Bloom filter (IBF)-based differential flow table synchronization method that represents the updated flow table as a compact IBF and transmits it to the switch only once. The switch reconstructs the updated flow table by decoding a difference-IBF (d-IBF), which is generated from the received IBF and the IBF constructed from its own local flow table. This differential synchronization mechanism reduces both communication overhead and update latency while preserving correct flow table reconstruction. The comparative analysis shows that the proposed method successfully reconstructs updated flow tables and reduces the communication cost by up to 49.35% compared with the conventional OpenFlow update method when installing 50 flow entries. Decoding experiments show that the proposed method sustains a decoding success rate above 99.9% for flow tables ranging from 1,000 to 1,000,000 entries. Mininet-based evaluations using OpenFlow confirm that the proposed method reduces actual control-channel communication cost in both single-switch and multi-switch environments, achieving reductions of up to 36.1% and 33.3%, respectively. Furthermore, to demonstrate the hardware implementation feasibility of the proposed method, we implement a d-IBF decoding hardware accelerator on an FPGA-based switch platform.	10.1109/TNSM.2026.3743515
Chengsheng Pan, Yingzhi Wang, Huaifeng Shi, Lishang Qin, Xiaosong Cui	DBSDD-AQM: Dynamic Buffer Sizing-Based Deep Deterministic Active Queue Management for Edge IoT Networks	2026	Early Access	Delays Satellite broadcasting Internet of Things	Bursty heterogeneous traffic can build persistent queues at edge IoT gateways when the offered load converges on a lower-rate bottleneck. Conventional active queue management (AQM) regulates packet dropping under a fixed buffer capacity, whereas adaptive buffer sizing alone does not coordinate capacity adjustment with queue-removal control. We propose Dynamic Buffer Sizing-Based Deep Deterministic Active Queue Management (DBSDD-AQM), which combines a bounded buffersizing loop with a faster continuous queue-removal controller. DBS adjusts the admissible capacity according to sustained queue evolution, while the DDPG Actor selects packet-granular removal actions for the resident backlog. A class-aware extension maps three concurrent service classes to logical FIFO queues sharing the same dynamic physical buffer. DBSDD-AQM is implemented using an NS3-PyTorch framework. Aggregate experiments compare it with RED, ARED, PIE, CoDel, and DQN-AQM, while the class-aware evaluation additionally includes FQ-CoDel and FQ-PIE. Matched-DBS comparisons show similar throughput but different delay–loss operating points among the aggregate controllers, and component ablation distinguishes the effects of capacity adaptation and learned queue removal. Relative to a fixed 50 KB buffer, DBS reduces average queueing delay by 16.23% for UDP and 17.70% for TCP Westwood in controlled transport experiments. In the evaluated three-class overload scenarios, all classes retain nonzero throughput. Class-aware DBSDD-AQM provides stronger D- and T-class differentiation, whereas FQ-CoDel and FQ-PIE produce more uniform class-level outcomes and stronger R-class performance. The measured 95th-percentile batch-one Actor inference latency is 0.109 ms on the reported x86-64 platform.	10.1109/TNSM.2026.3741334
Yifan Zeng, Zihui Gu, Peijia Zheng, Huiyu Zhou	A Federated Semi-Supervised Framework for Data Efficiency and Privacy-Preserving Intrusion Detection in Internet of Things	2026	Early Access	Internet of Things Modeling Labeling	The Internet of Things (IoT) forms the foundation of modern smart environments, enabling intelligent industries, homes, cities, healthcare, and autonomous systems. However, as IoT devices increasingly connect to cloud platforms and the Internet, they become highly vulnerable to network intrusions that can lead to critical data breaches and service disruptions. This paper proposes a Contrastive Federated Semi-Supervised Learning Network Intrusion Detection framework (CFedSSL-NID), specifically tailored to enhance the security of IoT communications. It addresses practical scenarios where clients lack labeled data and computational resources, and where data privacy is paramount. The CFedSSL-NID framework enhances detection performance and robustness. It uniquely achieves this by synergistically leveraging abundant, unlabeled data from distributed IoT gateways through self-supervised contrastive learning, while optimally integrating supervised signals from a central server. This dual approach enables highly accurate intrusion detection while substantially mitigating the reliance on scarce and costly labeled data. Experiments on the TON-IoT, UNSW-NB15, and NSL-KDD datasets show that CFedSSL-NID outperforms federated and centralized baselines; additional evaluations under Non-IID data, asynchronous participation, and malicious-client updates confirm robustness, while communication and edge-inference analyses indicate its deployment feasibility.	10.1109/TNSM.2026.3742261
Chenxin Zhou, Cheng Huang, Qiang Zhang, Qiliang Xie, Zeqing Li	NESTTOR: Detecting Tor-over-Proxy Traffic with Factorized Decisions	2026	Early Access	Fluid flow Labeling Protocols	Tor users can place an encrypted proxy between the client and the Tor network to conceal the exposed entry connection. This additional layer also transforms the Tor byte stream through protocol framing, buffering, and TCP segmentation before an observer sees the outer client-to-proxy flow. The transformation breaks stable packet-to-cell alignment and raises two questions. We ask which Tor behaviors remain observable across different proxies and how a detector should combine the presence of an outer proxy with the presence of Tor traffic. Existing datasets primarily study Tor and proxy traffic as separate targets and provide limited support for answering these questions under a unified evaluation protocol. We propose NESTTOR, which trains a proxy gate and a proxy-conditioned Tor detector separately and combines their decisions with an AND rule. Its stage-based features capture relative connection evolution without reconstructing Tor cells. We construct a longitudinal benchmark of 349,891 bidirectional TCP flows that distinguishes Tor-over-Proxy (ToP), Pure Proxy, Direct Tor, and Ordinary traffic across four proxy protocols, 1,663 domains, and approximately 18 months. NESTTOR achieves 99.45% macro-F1 on confirmatory data from unseen domains and 88.50% on later captures.	10.1109/TNSM.2026.3743261
Lei Liu, Yong Wang, Qian He, Junqi Chen, Yuming Liu, Baokang Zhao	Accelerating Full-Node Repair in Erasure-Coded Storage via Adaptive In-Network Aggregation	2026	Early Access	Maintenance engineering Bandwidth Codes	The erasure-coded data redundancy mechanism is extensively adopted in distributed storage systems to guarantee data reliability with low storage redundancy. However, recovering a failed node (full-node repair) inherently incurs high network overhead and latency. Existing full-node repair methods typically allocate tasks at block granularity, making it difficult to align each helper’s transmitted data volume with its available upload bandwidth. In addition, receiver-side download bottlenecks further limit repair throughput. To address these challenges, we propose FinRepair, a full-node repair framework that couples fine-grained task allocation with adaptive in-network aggregation (INA). FinRepair first formulates upload task allocation as a constrained max-min problem, and solves it with an iterative maximum flow-based algorithm, which determines the sub-block sizes and helper assignments. It then employs a bipartite graph-based placement strategy to scatter reconstructed blocks across storage nodes, and performs in-network aggregation for selected sub-blocks to satisfy each receiver’s download constraint. We establish a necessary and sufficient condition for upload allocations and prove the optimality of the selective in-network aggregation rule. We implement FinRepair with a Tofino programmable switch and 16 storage nodes. Experimental results show that FinRepair improves repair throughput by 16.85%–46.20% compared with state-of-the-art methods, while applying in-network aggregation to only 22.86%–32.43% of sub-blocks.	10.1109/TNSM.2026.3743621
Huiqi Zhao, Zhiying Sun, Gang Liu, Fang Fan	PMDC: A Prior Knowledge Constrained Multimodal Method for Encrypted Malicious Traffic Detection Integrating Difficulty Awareness and Confusion Rectification	2026	Early Access	Modeling Payloads Fluid flow	Encrypted network traffic is increasingly exploited for covert malicious activities, complicating detection. Current approaches often rely on black-box models that make limited use of domain knowledge and struggle to distinguish similar malware families, leading to performance degradation under evolving traffic distributions. We propose PMDC, a dual-branch multimodal model for encrypted malicious traffic detection at the bidirectional-flow level. PMDC uses an improved MobileViT branch to capture spatial patterns from the spatialized payload representation with header-derived priors and flow-level statistical knowledge, while a Temporal Convolutional Network (TCN) branch captures fine-grained packet-level temporal patterns and long-range behavioral dependencies. A difficulty-aware global fusion mechanism selects a single pair of branch weights on the validation set by considering classification confidence, uncertainty, and historical stability, and applies the selected weights to all test samples. Additionally, a directed low-rank residual rectification module (CARM) helps separate easily confused malware families while largely preserving the decisions for other classes. Experiments on four benchmark datasets show that PMDC achieves consistently strong accuracy, F1-score, TPR, and FPR compared with representative baselines, and shows non-catastrophic degradation under packet-loss perturbations. On a small self-built power-grid dataset collected from a single attack incident, PMDC correctly identifies all 64 malicious flows with a false positive rate of 0.73%, providing preliminary evidence of binary cross-scenario detection capability under this limited setting.	10.1109/TNSM.2026.3742766
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
Shigen Shen, Muxi Li, Yizhou Shen, Jingnan Dong, Wenlong Ke, Ruilong Deng, Tian Wang	DeSPU: Privacy-Compliant Decentralized Sparse Personalized Federated Unlearning for Edge Intelligence-Enabled IoT Networks	2026	Early Access	Internet of Things Modeling Maintenance engineering	Edge intelligence-enabled Internet of Things (Edge-IoT) increasingly relies on decentralized on-device learning to reduce cloud dependence and keep locally collected IoT data on terminal devices. However, deployed models may still memorize privacy-sensitive class-level knowledge and must support deletion requests after training. This paper studies class-level federated unlearning in a serverless Edge-IoT workflow where each IoT client maintains a sparse personalized IoT-local model and exchanges model information only with neighbors. We propose DeSPU, a decentralized sparse personalized federated unlearning framework under a practical deletion-window protocol. DeSPU estimates a target-class direction with a lightweight dual-model mechanism, applies semantic-layer adaptive linear unlearning to suppress target knowledge, and performs topology-aware local repair regularized by cached neighbor snapshots collected during routine peer-to-peer training. The online unlearning path does not store historical gradients and does not introduce additional post-request model communication beyond routine decentralized exchanges; the one-time β calibration scan is reported separately, and the calibrated β can be reused under the same configuration. To propagate deletion status beyond the requester, DeSPU also attaches a metadata-only forgetting flag to those routine exchanges and invalidates affected cached neighbor references at receiving clients. Experiments on CIFAR-10, MNIST, CIFAR-100, and CINIC-10 show near-zero target-class accuracy with high retained accuracy, up to ∼ 4.7 × 103 speedup over full retraining on CIFAR-10, and membership-inference ROC-AUC close to random guessing. Additional analyses over seeds, requester clients, target classes, cache staleness, sparsity, communication, and hardware cost further demonstrate the practicality and limitations of DeSPU for privacy-compliant Edge-IoT deployments. The project link is available at https: //github.com/aaaM2002/UN_CODE.	10.1109/TNSM.2026.3743397
Mohammad Saberi, Ali Ahmadi Esfidi, Mahdi Dolati, Naeimeh Omidvar	FlexINA: In-Network Aggregation for Accelerating Distributed Machine Learning with Flexible Routing	2026	Early Access	Modeling Switches Timing	The growing scale and complexity of modern machine learning (ML) models have rendered distributed training indispensable. However, as model dimensions expand, communication overhead increasingly dominates the overall training time, creating a critical performance bottleneck. While techniques such as gradient compression offer partial relief, they often incur substantial computational overhead or require specialized hardware. In-network aggregation (INA) has emerged as a compelling alternative, offloading partial computations—such as gradient reduction—directly onto programmable network switches. Yet, current INA frameworks predominantly rely on rigid, static routing schemes or uncoordinated, opportunistic aggregation, fundamentally limiting their capacity to exploit the full potential of programmable data planes. To address this limitation, we formulate the Joint Scheduling-Routing for In-Network Aggregation (JINA) problem. JINA introduces a time-slotted execution model to enable fine-grained temporal scheduling synchronously with spatial routing. By leveraging source routing, it dictates perpacket forwarding paths while explicitly coordinating multi-hop partial aggregation across the network. Furthermore, we propose FlexINA, a scalable optimization framework that solves the JINA problem through a three-phase dimensionality reduction strategy: host-level pre-aggregation, topological switch filtration, and temporal window partitioning. Extensive evaluations across fifteen diverse network topologies demonstrate that FlexINA reduces the total network load by up to 8.3% compared to INA baselines, while maintaining high computational efficiency.	10.1109/TNSM.2026.3740503
Mohammad Khosravi, Setareh Maghsudi	A Robust Optimization Approach for Regenerator Placement in Fault-Tolerant Networks Under Discrete Cost Uncertainty	2026	Early Access	IP networks Costing Costs	We focus on robust, survivable communication networks, where network links and nodes are affected by an uncertainty set. In this sense, any network links might fail. Besides, a signal can only travel a maximum distance before its quality falls below a certain threshold, necessitating its regeneration by regenerators installed at network nodes. In addition, the price of installing and maintaining regenerators belongs to a discrete uncertainty set. Robust optimization seeks a solution with guaranteed performance against all scenarios modeled in an uncertainty set. Thus, the problem is to find a subset of nodes with minimum cost for the placement of the regenerator, ensuring that all nodes can communicate even if a subset of network links fails. To solve the problem optimally, we propose two solution approaches, including one flow-based and one cut-based integer programming formulation, as well as their iterative exact method. Our theoretical and experimental results show the effectiveness of our methods.	10.1109/TNSM.2026.3740028
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
Taylor Henderson, Eric Osterweil, Pavan Dinesh, Robert Simon	Scalable and Robust User Deobfuscation using Pattern of Life	2026	Early Access		Online message boards have become increasing popular with cyber criminals and those wishing to conduct illegal acts. They are used in coordinating drug/human trafficking, terrorist activity, and distributing information from data breaches to name a few. We provide a novel method for deobfuscating message board users: Deobfuscating Using Patterns of Life (DUPOL). DUPOL uses a novel technique to take advantage of system artifacts arising from the proliferation of Public DNS. DUPOL is validated on a test bed which accurately maps the Internet to different information scopes. This provides an understanding of how DUPOL would perform if it was performed by different entities on the Internet. DUPOL is shown to be able to correctly identify message board users even when users employ privacy preserving technologies using only observable network traffic and without the need to decrypt any information. Additionally, DUPOL is shown to be robust to missing data and scalable using a novel decomposition of the NCC which removes the need for pairwise comparison.	10.1109/TNSM.2026.3744089