TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Hussein Fawaz, Jacopo Talpini, Marco Savi, Silvia Giordano, Omran Ayoub	Detecting Zero-Day Attacks via Reconstruction of Feature Influence and Model Uncertainty	2026	Early Access	Modeling Uncertainty Training	In practical Network Intrusion Detection System (NIDS) deployments, detecting anomalies is only the first step, while determining the exact nature of those anomalies is equally important. Commonly, anomalous traffic is forwarded to a supervised multiclass classifier trained to identify known attack categories. While effective for known threats, this step presents a significant limitation, as zero-day attacks can be misclassified as known attacks. Therefore, there is a need for approaches that go beyond standard classification and can reliably recognize when an input does not conform to any learned attack pattern, i.e., zero-day attacks. To tackle this problem, we propose a novel detection strategy that leverages per-instance feature importance scores from an explainable Artificial Intelligence (XAI) framework and prediction uncertainty estimates derived from an ensemble classifier. To evaluate our approach, we conduct extensive experiments using a leave-one-attack-out strategy across three benchmark datasets, CICIoT2023, NF–TON–IoT, and CIC–DDoS2019, and test performance under two underlying classifiers, namely XG-Boost and Random Forest, demonstrating the model-agnostic nature of our method. Experimental results show that our approach achieves best-case AUROC gains approaching 40% and F1-score improvements of up to 73%, while maintaining positive or near-neutral worst-case performance across datasets, highlighting the effectiveness and robustness of jointly modeling explanation-driven reconstruction error and predictive uncertainty for reliable zero-day threat identification.	10.1109/TNSM.2026.3731401
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
Junior Momo Ziazet, Brigitte Jaumard	Energy Efficient Placement of Logical Functionalities in 5G Networks	2026	Early Access	Energy Copper Modeling	Although 5G networks are more efficient in terms of power consumption to traffic ratio, efforts still need to be made to further increase energy efficiency not only for the radio part, but also with respect to the growing cloud component with edge servers. Consolidation of traffic workloads onto shared infrastructures is a key feature of cloud computing to reduce energy consumption, and logical functionality placement plays a key role in this regard. Here, in the cloud RAN context, we propose a unified and energy-aware logical placement of 5G E2E functionalities, i.e., distributed units (DUs), centralized units (CUs), and user plane functions (UPFs), together with traffic routing. The placement problem is formulated as a large-scale integer linear program and solved using a column generation-based decomposition technique, complemented by an efficient heuristic to ensure tractability and improved scalability. The model captures key network and cloud (compute) resources, jointly optimizing the placement of DU, CU, and UPF components, along with traffic routing, to minimize energy consumption while maintaining low latency and high Quality of Service (QoS). Numerical results, based on an open Montreal traffic dataset, demonstrate that the proposed column generation algorithm achieves near-optimal solutions, while the heuristic approach offers significantly better scalability with consistently strong performance. The proposed methods reduce energy consumption by up to 14% and maintain low-latency service delivery. Furthermore, the results highlight that static, peak-time-based placement strategies can lead to inefficiencies throughout the day, emphasizing the importance of accounting for broader temporal traffic patterns.	10.1109/TNSM.2026.3729149
Mubashir Murshed, Glaucio H. S. Carvalho, Robson E. De Grande	Holistic Intelligent Traffic Steering Management in Multi-RAT Vehicular Networks	2026	Early Access	Radio access technologies Rats Vehicles	Multiple Radio Access Technology (multi-RAT) environments provide a promising foundation for service-aware communication in intelligent transportation systems (ITS) and smart cities. However, traffic steering (TS) in highly mobile and ultra-dense vehicular networks remains challenging due to dynamic network conditions, heterogeneous RAT capabilities, varying vehicle requirements, packet loss, latency, and frequent ping-pong RAT switching. In this context, we propose Holistic Intelligent Traffic Steering (HITS), a proactive bi-level TS management framework for multi-RAT vehicular networks. HITS integrates centralized network-wide guidance with local vehicleside decision-making. At the central level, a Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model captures holistic spatio-temporal network dynamics and evaluates RAT optimality. At the local level, a State-Action-Reward- State-Action (SARSA) reinforcement learning agent performs adaptive, vehicle-specific RAT selection using local observations and central-level optimality guidance. Results show that HITS achieves up to 6.5% higher average throughput, reduces packet loss ratio by more than 30.2%, lowers latency by nearly 12.2%, and reduces the ping-pong RAT switching rate by over 24% compared with baseline and state-of-the-art (SoTA) TS approaches.	10.1109/TNSM.2026.3729840
Ahmed Rjiba, Hicham Lakhlef, Joachim Bruneau-Queyreix, Meriem Afif	Federated Learning in Fog Computing within IoT Environments: An up-to-date and comprehensive survey	2026	Early Access	Federated learning Internet of Things Edge computing	The Internet of Things (IoT) connects diverse, resource-constrained devices, driving innovation in domains such as healthcare, smart cities, and industrial automation. However, the exponential growth of IoT devices poses critical challenges in data processing, privacy, security, and latency. Fog Computing (FC) mitigates these issues by decentralizing computational resources, processing and storing data locally to enable low-latency, high-quality services. This makes FC an ideal platform for integrating Federated Learning (FL), a decentralized machine learning paradigm that trains models locally on IoT devices and shares only aggregated updates, preserving data privacy. Since its introduction, FL has garnered considerable attention for enabling privacy-preserving collaborative model training in distributed environments. The convergence of IoT, FC, and FL offers substantial opportunities to advance IoT system performance, but it also presents challenges in resource allocation, security, energy efficiency, computational complexity, and system heterogeneity. This survey provides a comprehensive and up-to-date analysis of the integration of FL and FC within IoT environments, exploring their synergies, challenges, and state-of-the-art advancements.We review critical aspects, including infrastructure enhancements, security mechanisms, and the emerging role of Digital Twin (DT) technology, which creates virtual replicas of IoT devices to optimize system efficiency and real-time performance. Through case studies in healthcare and smart cities, we highlight practical applications of FL-FC integration. We compare our work with existing surveys, highlight its specific focus on the FL-FC-IoT-DT convergence, and identify open challenges and future research directions toward secure, scalable, and intelligent IoT ecosystems.	10.1109/TNSM.2026.3731410
Chen Jue, Yang Tiancheng, Rao Yirui, Qiu Xihe, Yan Fengting, Chen Shanshan, Jiang Xiaoyan	Snow Ablation Optimization Tackles Controller Placement Problem: Optimizing Propagation Latency and Load Balance in SDN	2026	Early Access	Loading Optimization Algorithms	The Controller Placement Problem (CPP) is critical in multi-controller Software-Defined Networking (SDN), as controller placement and switch-controller mapping directly affect propagation latency and load balance. To address this problem, this paper introduces, for the first time, Snow Ablation Optimization (SAO) into CPP and develops three specialized algorithms, namely SAO-RPL, SAO-MLB, and SAO-OMO. SAO-RPL determines controller locations to minimize controller-switch latency, SAO-MLB dynamically adjusts switch-controller mappings to balance controller loads, and SAO-OMO jointly optimizes latency and load imbalance. Experiments on real-world network topologies show that SAO-RPL obtains near-optimal solutions with a maximum error of 0.45% relative to the global optimum and exhibits stable performance over 30 independent runs. SAO-MLB reduces the difference between the maximum and minimum normalized controller loads by at least 49.53% and maintains low run-to-run variability under different numbers of controllers. SAO-OMO reduces load imbalance by up to 36.57% while limiting the maximum latency increase to 1.34%, demonstrating an effective trade-off between propagation latency and load balance.	10.1109/TNSM.2026.3733376
José Antonio Pastor Valera, Martin Husák, Jesús García Rodríguez, Jorge Bernal Bernabé, Antonio Skarmeta	Real Time Cyber Situational Awareness for 6G Networks Leveraging Spatial Metrics	2026	Early Access	Modeling Timing Fluid flow	The dynamic and heterogeneous nature of 6G networks demands continuous, real-time cyber situational awareness (CSA) to support cognitive security operations such as behavior analysis, threat hunting, and adaptive defense. Traditional CSA frameworks like CRUSOE capture structural and mission-level data but cannot process the dynamic, high-frequency telemetry typical of 6G environments. This paper presents the Extended Infrastructure and Service Information Model (EISIM) and its implementation within a Cyber Situational Awareness Platform (CSAP) designed for real-time, context-aware security management. CSAP aggregates and models data from diverse sources into a unified graph representing assets, flows, services, and vulnerabilities, enabling continuous assessment of operational and security posture. The platform introduces novel spatial risk metrics, including Flow Load Centrality and Operational Risk Centrality, which integrate topological position, traffic intensity, vulnerability exposure, and resource capacity to quantify risk, exposure, and criticality. By combining workflow-driven data collection with real-time spatial analytics, CSAP enhances situational awareness and decision-making within 6G Security Operation Centers (6G-SOCs), enabling proactive identification of critical network elements and improved resilience through cognitive, data-driven management.	10.1109/TNSM.2026.3734629
Larisa-Mihaela Tufeanu, Marius-Constantin Vochin, Frank Y. Li	Residual Artifact Governance With ML-Enabled Garbage Collection Prediction in Kubernetes-Based 5G/6G Service-Based Architecture	2026	Early Access	Management Retrieval augmented generation Monitoring	The evolution of the service-based architecture (SBA) requires cloud-native core networks, and its open and modular design nature makes Kubernetes the most eminent platform for SBA implementation. One critical-yet-underexplored issue when deploying the SBA based on Kubernetes is the accumulation of residual artifacts that persist and degrade observability and stability inside Kubernetes clusters. In this paper, we propose residual artifact governance (RAG), a non-intrusive add-on module to the SBA that enforces bounded garbage collection (GC) policies without modifying the underlying architecture. The module follows a master–agent structure: a GC-master derives data-driven machine learning-enabled cleanup policies using the GC data provided by GC-agents, while GC-agents co-located at each network function execute deterministic, auditable cleanup actions. As a proof-of-concept demonstration, we implement a Kubernetes-based prototype to validate the operability and feasibility of RAG. While a machine learning predictor located at the GC-master provides early warning signals on time-to-collapse caused by the accumulation of residual artifacts, periodic fractional cleanup actions at GC-agents keep residual peaks bounded under both nominal and stress leak regimes. Results reveal that the accumulation of residual artifacts can be measured, predicted, and controlled using Kubernetes-native mechanisms through our RAG enhancement while remaining compatible with 3GPP-aligned observability and stability assurance principles.	10.1109/TNSM.2026.3734448
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
Kim Hammar, Neil Dhir, Rolf Stadler	Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Search	2026	Early Access	Modeling Timing Trees (botanical)	The CAGE-2 challenge is considered a standard benchmark to compare methods for autonomous cyber defense. Current state-of-the-art methods evaluated against this benchmark are based on model-free (offline) deep reinforcement learning techniques, which do not provide provably optimal defender strategies. We address this limitation and present a formal (causal) model of CAGE-2 together with a method that converges to a provably optimal defender strategy, which we call causal partially observable Monte-Carlo planning (C-POMCP). Our method has two novel properties. First, it incorporates the causal structure of the target system through causal relationships among the system variables. This structure allows for a significant reduction of the search space of defender strategies. Second, it is an online method that updates the defender strategy at each time step via tree search. Evaluations against the CAGE-2 benchmark show that C-POMCP achieves state-of-the-art performance with respect to effectiveness and requires two orders of magnitude less computation than the closest competitor method.	10.1109/TNSM.2026.3735865
Marco Garofalo, Luca D’Agati, Laura García, Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez, Joan Garcia-Haro, Antonio Puliafito, Giovanni Merlino, Francesco Longo	Trustless SLA Enforcement and Roaming in LoRaWAN through Smart Contracts	2026	Early Access	Roaming Service level agreements LoRaWAN	LoRaWAN is widely used for Internet of Things (IoT) services that require long-range, low-power wireless connectivity. As deployments grow, roaming between different network operators becomes increasingly important to maintain service continuity for mobile IoT devices. In practice, however, roaming still depends on bilateral agreements and trusted intermediaries, which limit scalability and reduce transparency in multi-operator settings. This work introduces a blockchain-based roaming architecture that uses Algorand smart contracts to automate Service Level Agreement (SLA) management between providers. The system supports dynamic roaming agreements, immutable packet accounting, and transparent settlement. In our system, the enforced guarantee concerns forwarding-level service quality at the roaming interface, namely payment conditional on observed delivery ratio, rather than deterministic radio-layer latency or jitter guarantees. We implemented the full infrastructure, including a custom Gateway Bridge that extracts the Network Identifier (NetID), a blockchain service that interacts with Algorand smart contracts for SLA validation, and a decentralized provider catalog for operator discovery. We evaluated the system on a testbed with production-grade ChirpStack network servers and compared it with our previous non-blockchain implementation. Both versions achieve comparable throughput (5800–5900 packets/minute with 1000 devices) and maintain 99% packet forwarding efficiency. Blockchain integration adds measurable overhead, including a forwarding latency overhead in the 400–490 ms range for SLA validation, largely independent of the underlying network delay, but remains acceptable for delay-tolerant IoT services. Overall, the results show that decentralized LoRaWAN roaming can be implemented without breaking compatibility with existing network architectures.	10.1109/TNSM.2026.3734694
Liwei Zhang, Tong Zhang, Xiaoqin Feng, Wenxue Wu, Hao Yang, Ping Liu, Yanying Ma, Fengyuan Ren	Leveraging Hot Standby Routing to Improve Reliability in TSN	2026	Early Access	Fluid flow Timing Joining processes	Time-Sensitive Networking (TSN) is widely deployed in industrial networks because it can provide deterministic transmission services for Time-Triggered (TT) flows. Link failures pose severe threats to the reliability of TT flows. Frame Replication and Elimination for Reliability (FRER) defined by IEEE 802.1 CB tolerates such failures by transmitting the same frames via disjoint paths, but this introduces excessive bandwidth overhead. To this end, we present a Hot Standby Routing (HSR) mechanism tailored for TSN to ensure the reliability of TT flows while minimizing bandwidth usage. Unlike FRER, HSR can locally reroute a single frame to achieve tolerance to link failures. Specifically, the primary and secondary paths are computed hop-by-hop for each TT flow and installed on the switches in the network. Under normal conditions, the secondary path is in a silent standby state. If the primary path fails, the affected TT flow will be seamlessly rerouted to the secondary path by the local switch for transmission. The simulation results show that HSR can provide highly reliable transmission for TT flows while significantly reducing bandwidth consumption. Furthermore, HSR exhibits stronger robustness in large-scale networks.	10.1109/TNSM.2026.3733170
Yifan Yang, Xionglve Li, Sizhuang Wu, Zhihuang Liu, Bo Yu, Bingnan Hou, Zhiping Cai	TracerForests6: Efficient IPv6 Alias Resolution via Traceroute Path Expansion	2026	Early Access	Probes Seeds (agriculture) IP networks	In recent years, IPv6 networks have seen rapid global deployment. Router-level topology is essential for understanding network structure and supports performance monitoring, security analysis, and fault diagnosis. A key step is alias resolution, which maps multiple IP addresses to the same router so that observed interface addresses are correctly attributed. However, the vast IPv6 address space presents new challenges for topology discovery. Traditional IPv4 approaches rely on exhaustive collection of router interface addresses followed by alias resolution. Many IPv6 methods inherit this approach and suffer poor scalability, limited efficiency, and reduced coverage at IPv6 scale. In this work, we present TracerForests6, a heuristic and efficient method for IPv6 alias resolution. It continuously discovers alias pairs across the network starting from high-confidence initial seed pairs. It analyzes traceroute paths to the initial set to identify candidate pairs from path characteristics, and a random forest trained on multidimensional path features validates them. In our evaluation on CAIDA ITDK and two RIPE Atlas traceroute snapshots, using the initial seed construction and 14-vantage-point probing configuration adopted in this work, TracerForests6 expands the identified alias set to 1.78× the corresponding initial seed set while maintaining an alias-pair identification accuracy of 96.85%. To facilitate reproducibility and follow-up research, we will release a set of public resources related to TracerForests6, including the implementation and measurement artifacts, which can support further IPv6 topology analysis.	10.1109/TNSM.2026.3731850
Bita Fatemipour, Zhe Zhang, Marc St-Hilaire	Adaptive Routing Optimization with Cost and Deadline Awareness Using Hierarchical Deep Reinforcement Learning	2026	Early Access	Costing Costs Routing	Timely and cost-efficient data transfers in large-scale networks remain challenging due to diverse topologies, non-uniform pricing models, and variable traffic demands. Existing literature often relies on multi-objective optimization, employing heuristic methods to reduce computational complexity; however, these approaches typically assume stable or predictable demand and struggle to scale effectively. Reinforcement Learning (RL) has been explored for its adaptability, yet many RL-based methods remain single-objective or topology-agnostic. This paper introduces CD-DRL, a hierarchical Deep RL framework that jointly optimizes transmission cost and deadline satisfaction, two objectives that often conflict in large-scale networks, through two cooperative agents. A routing agent, built on a Graph Neural Network, selects paths over a structured, multi-binary action space, enabling topology-aware routing across varying network scales and demand patterns. An adaptive tuning agent observes network state and recent performance to dynamically adjust the cost-deadline tradeoff to best fit current conditions. This hierarchical design allows CD-DRL to respond to dynamic network events such as congestion and bandwidth fluctuations, where no single fixed tradeoff remains optimal. We validate CD-DRL through extensive experiments on diverse backbone topologies and request distributions under static and time-varying network conditions. Compared with a state-of-the-art GNN-based RL method and traditional heuristics, CD-DRL improves the deadline-met ratio by up to 25% while maintaining competitive total cost and demonstrating strong scalability. Additionally, CD-DRL achieves faster execution time than mathematical optimization baselines, enabling high-throughput, latency-sensitive routing in dynamic environments.	10.1109/TNSM.2026.3731031
Stephen Jasina, Loqman Salamatian, Joshua Mathews, Scott Anderson, Paul Barford, Mark Crovella, Walter Willinger	Matisse: Visualizing Measured Internet Latencies as Manifolds	2026	Early Access	Manifolds Internet Measurement	Manifolds are complex topological spaces that can be used to represent datasets of real-world measurements. Visualizing such manifolds can help with illustrating their topological characteristics (e.g., curvature) and providing insights into important properties of the underlying data (e.g., anomalies in the measurements). In this paper, we describe a new methodology and system for generating and visualizing manifolds that are inferred from actual Internet latency measurements between different cities and are projected over a 2D Euclidean space (e.g., a geographic map). Our method leverages a series of graphs that capture critical information contained in the data, including well-defined locations (for vertices) and Ricci curvature information (for edges). Our visualization approach then generates a curved surface (manifold) in which (a) geographical locations of vertices are maintained and (b) the Ricci curvature values of the graph edges determine the curvature properties of the manifold. The resulting manifold highlights areas of critical connectivity and defines an instance of “Internet delay space” where latency measurements manifest as geodesics. We describe details of our method and its implementation in a tool, which we call Matisse, for generating, visualizing and manipulating manifolds projected onto a base map. We illustrate Matisse with three case studies: a simple example to demonstrate key concepts, and visualizations of the US and Europe public Internet to show Matisse’s utility.	10.1109/TNSM.2026.3730274
Xiaodi Wang, Yunwei Dong, Weizhi Meng, Meng Li, Yining Liu	Dropout-Tolerant Privacy-Preserving Aggregation for Federated Mobile Crowdsensing	2026	Early Access	Modeling Privacy Internet of Things	Federated Learning (FL) has emerged as a key enabler for privacy-preserving, decentralized sensing systems, giving rise to Federated Mobile Crowdsensing (F-MCS). A well-known bottleneck in such systems is the inefficiency of synchronous training, which stalls for all participants and is susceptible to stragglers in heterogeneous environments. Although asynchronous FL methods have been explored to alleviate this, they often introduce the critical issue of stale updates, which can degrade model convergence and accuracy. To simultaneously address the challenges of efficiency, staleness, and robustness, this paper proposes a novel Dropout-Tolerant Privacy Aggregation (DTPA) scheme for FL that operates without a trusted third party (TTP). Our solution leverages the distributed decryption feature of the lifted EC-ElGamal cryptosystem to enable secure, decentralized model aggregation. We further introduce an efficient worker selection algorithm to systematically reduce waiting time. Moreover, a dedicated dropout-tolerant mechanism is developed to maintain protocol execution even under a high rate of client failures, thereby enhancing robustness. Security analysis confirms that our scheme fulfills essential privacy and security requirements. Extensive simulations demonstrate that the proposed DTPA scheme significantly improves training efficiency and convergence stability compared to state-of-the-art methods, while remaining practical for deployment on resource-constrained mobile devices.	10.1109/TNSM.2026.3732465
Jianer Zhou, Xinyi Qiu, Zhenyu Li, Gareth Tyson, Encheng Yu, Weichao Li, Heng Pan, Xinyi Zhang, Zhiwei Xu	Themis: An Adjustable Congestion Control Framework for Improving Video QoE	2026	Early Access	Quality of experience Videos Fluid flow	Optimizing congestion control algorithms (CCAs) has the potential to enhance video quality of experience (QoE). The goal of this work is to devise a congestion control framework that (i) ensures that individual users enjoy high video QoE, while (ii) minimizing variance, such that QoE is fairly distributed across all users, especially in fluctuating network, such as cellular network. We present Themis, a video-centric congestion control framework. Themis first uses a distributed approach to allocate a fair target QoE for each client. Based on this fair QoE, Themis then selects congestion control actions to optimize for video QoE (rather than throughput) based on application-layer signals provided by the client. Thus, rather than trying to maximize a flow’s (fair) share of bandwidth, Themis optimizes a flow’s share of the QoE budget. We evaluate Themis in both emulated and production networks. We show that in cellular network Themis achieves a 12.4% QoE improvement compared with BBR, and 37.1% QoE standard deviation decrease compared with the state-of-the-art, Minerva.	10.1109/TNSM.2026.3732350
Siyu Jiang, Feng Guo, Di Chen, Yuan Liu, Ying Chen, Weijun Sun, Yu Wang, Shen Su	Smart Contract Vulnerability Detection via Mask Consistency with Dynamic Margin Adjustment	2026	Early Access	Labeling Modeling Smart contracts	With the rise of smart contract applications, new attacks that exploit contract vulnerabilities continue to emerge, and effective vulnerability detection methods are urgently needed. Deep learning-based methods have shown excellent performance. However, for new types of vulnerabilities, due to the lack of real labels to help the model learn subtle code differences, previous methods have difficulty distinguishing between vulnerable contracts and safe contracts with similar key code segments, resulting in false negatives. To address this problem, this paper proposes a smart contract vulnerability detection method that uses mask consistency (MC) and dynamic margin adjustment (DMA). Unlike traditional Masked Language Modeling (MLM) in CodeBERT that performs token-level reconstruction for general representation learning, our MC enforces classification-level consistency between a masked student network and an unmasked EMA teacher network at the semantic graph block level under semi-supervised domain adaptation. This enhances the model’s discriminative ability by adding contextual information of similar code segments as additional clues. Specifically, we define a student network to learn masked contracts, a teacher network to learn complete contracts, and implement few-shot learning through semi-supervised domain adaptation. In this process, the student network is helped to learn to correctly distinguish similar contracts by fusing contextual information. In order to guide students more effectively, we use DMA to screen high-quality pseudo-labels. We conduct extensive experiments on open source real-world vulnerability datasets, and the results show that our method significantly outperforms current mainstream deep learning methods in detecting six types of vulnerabilities. This approach also pioneers the application of domain adaptation and integrates MC with DMA in vulnerability detection, providing guidance for detecting different types of vulnerabilities.	10.1109/TNSM.2026.3733072
Yao Xin, Yuqiao Luo, Shufan Cao, Chongwu Dong, Qingfeng Tan	HBT: A Hybrid Bidding Tree for High-Performance Packet Classification	2026	Early Access	Heterojunction bipolar transistors Trees (botanical) Vegetation	Traditional packet classification algorithms based on decision trees often rely on rule replication to increase lookup speed, which inevitably leads to memory explosion. Conversely, existing zero-replication methods frequently suffer from extreme tree depth and structural fragmentation. To address this dilemma, this paper proposes the Hybrid Bidding Tree (HBT), a high-performance architecture designed to enforce zero-replication while sustaining deterministic lookup throughput. First, HBT employs an Overlap-Aware Rule Decomposition (OARD) framework to proactively isolate topologically entangled rules, purifying the primary geometric space. Second, HBT introduces a dynamic competitive bidding mechanism for tree construction. At each node, a discrete bit-selection path and a continuous range-partitioning path compete to determine the optimal splitting strategy based on local geometric heterogeneity. Finally, to guarantee an O(N) memory boundary, unpartitionable residual rules are assigned to a single-level Onion-Peeling fallback structure, preserving linear memory growth while introducing additional sequential checks in the auxiliary path. Experimental evaluations on ClassBench-ng rulesets containing up to 256k rules demonstrate the efficacy of HBT. Compared with state-of-the-art algorithms such as PT-Tree and TupleTree, HBT achieves the highest lookup throughput across all twelve evaluated rulesets at both the 128k and 256k scales, while maintaining strong memory efficiency and highly competitive construction and update latencies.	10.1109/TNSM.2026.3734240