TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Huijuan Zhu, Chenhao Zheng, Zhongyuan Liu, Yuan Zhang	Reliable Interpretations of Deep Learning-based Malware Detectors via Deep Q-Networks	2026	Early Access	Malware Signal detection Modeling	Deep learning has become widely used in Android malware detection, but its black-box nature raises trust concerns, limiting its use in critical security areas. To address this, various interpretation methods have been proposed. Unfortunately, these solutions often suffer from inconsistent results and poor adaptability to model updates. In this work, we propose XDQNMal, a Deep Q-Networks (DQN)-based global interpretation framework designed to uncover the critical features that drive decisions in deep learning-based malware detectors. To enhance the reliability of interpretation, XDQNMal captures API call frequency features derived from the runtime behavior of each application (App). Then, it unites a DQN model with the TabPFN detection model to work collaboratively, using variations in detection results as reward signals. These signals guide the DQN model to gradually identify the most impactful features as interpretations for the detection model’s decisions. Our experimental evaluation on real-world datasets demonstrates that the proposed XDQNMal framework generates reliable interpretation for deep learning-based malware detection models. For instance, suppressing the critical features identified by XDQNMal leads to an average decrease of 20.30% in the probability that the malicious sample is predicted as malicious, highlighting the pivotal role these features play in the model’s decision-making.	10.1109/TNSM.2026.3699408
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
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
Yuanhao Liu, Fen Zhou, Micha³ Pi´oro, Cao Chen, Tao Shang, Juan-Manuel Torres-Moreno	Power-Efficient Directed p-Cycle Design Leveraging Loop-Eliminating Flow and Column Generation	2026	Early Access	Joining processes Modeling Protection	As Internet traffic patterns exhibit increasing asymmetry, the directed pre-configured cycle (directed p-cycle) has demonstrated superior effectiveness and flexibility for protection in elastic optical networks (EONs). This paper addresses directed p-cycle protection against single-link failure using a just-enough modulation format (MF) adaptation approach. Unlike the conventional methods that rely on an estimated upper bound for the protection path length of a directed p-cycle, our method accurately calculates the exact length. We introduce a novel mixed integer linear programming (MILP) formulation incorporating a strategically designed loop-eliminating flow (LEF) model, eliminating the need for candidate cycle enumeration. The objective is to jointly minimize power consumption and spare spectrum usage. To solve large-scale instances, we propose two column generation (CG) approaches: MILP-CG, which generates columns via the MILP model and provides a guaranteed performance bound, and De-CG, which uses a fast heuristic decomposition algorithm for high efficiency and scalability. Numerical results show that our method achieves up to 37.14% performance improvement under asymmetric traffic. The proposed CG approaches also exhibit high computational efficiency and near-optimal performance for large-scale traffic.	10.1109/TNSM.2026.3704661
Ibirisol Fontes Ferreira, Cassio Vinicius Serafim Prazeres, Maycon Leone Maciel Peixoto, Eiji Oki, Gustavo Bittencourt Figueiredo	Narrow: A Fair Routing Multicast Algorithm for Distributed Interactive Applications in Edge Networks	2026	Early Access	Delays Algorithms Timing	Recent research in networking has increasingly focused on addressing the challenges of edge network services. A crucial issue in this context is routing, which must account for quality-of-service requirements. In particular, multicast routing provides optimized network services for groups of people using the same application, which is advantageous for operators and application providers. However, latency-constrained routing poses challenges when integrating diverse requirements into the routing computation, particularly when fairness among users is required. This work addresses the fairness requirement in multicast-overlaid and virtualized networks by presenting a solution that improves the equity of group interactions in the routing service. Our proposal, named Narrow, achieves fairer group interaction by selecting improved path options for multicast routing in edge networks. We compared Narrow with the Fair Shortest Path Tree (FSPT) and Chains algorithms from related studies on delay-constrained routing. Simulations indicated that Narrow reduced the inter-destination delay deviation by up to 84% and 49% relative to FSPT and Chains, respectively, across topologies of varying sizes. Similarly, Narrow improved by more than 99% against FSPT and by 70% against Chains across topologies with varying node degrees. Depending on the number of allowed alternative paths, Narrow reduced the inter-destination delay deviation by more than 99% compared with FSPT and by 38% compared with Chains. In emulated distributed interactive application session experiments, Narrow delivered the fairest response time, reducing it by 89% and 86% relative to FSPT and Chains, respectively. Furthermore, fairness in players’ scores improved by 20% and 16%, respectively, yielding more equitable group interaction from the application’s perspective.	10.1109/TNSM.2026.3704927
Heng Xu, Chengze Du, Zhiwei Yu, Letian Li, Ying Zhou, Bo Liu, Jialong Li	Distributed Flow Control for Efficient DNN Training Scheduling	2026	Early Access	Schedules Scheduling Training	Distributed Deep Neural Network (DNN) training generates periodic, long-lived, and interdependent flows that contrast sharply with the short, bursty, and independent flows typical of traditional cloud services. Existing flow scheduling methods, optimized for cloud traffic, struggle to handle the structured communication of DNN workloads, while static schedulers remain brittle under the computation jitter and stochasticity inherent in multi-tenant AI clusters. We propose a distributed traffic control and scheduling framework called PQ, which shifts from fragile global synchronization to a token-based queuing concept. PQ utilizes standard priority queues in commercial switches as elastic buffers, dynamically mapping task urgency to traffic priorities based on specific scheduling policies, such as minimizing waiting time, thereby accelerating efficiency. Results show that PQ achieves stable communication interleaving 3.6× to 8.8× faster than reactive baselines like MLTCP and FQ. Furthermore, it significantly optimizes performance by reducing average iteration time by up to 29.2% while maintaining higher link utilization.	10.1109/TNSM.2026.3704403
Weina Meng, Jiawen Shi, Xiaoqun Chen, Weinan Liu, Jiangjun Yuan	Time Period Selected Aggregation for Providing Hierarchical and Differentiated Services in Mobile Sensing	2026	Early Access	Modeling Timing Protocols	With the advancement of smart terminals and wireless networking technologies, mobile sensing has gained increasing popularity. A myriad of applications have emerged based on mobile sensing, with particular attention being drawn to data aggregation applications. Over the years, numerous studies have been conducted, ranging from initial approaches that did not address the issue of untrusted aggregators to more recent solutions capable of handling such challenges. In this paper, we introduce two novel types of data aggregation applications designed to offer hierarchical and differentiated services, alongside proposing two corresponding protocols equipped with privacy-preserving capabilities. These protocols ensure the protection of mobile users’ privacy concerning their sensed data in the presence of an untrusted aggregator, and are resilient against collusion attacks. Our protocols achieve constant key storage overhead (only 1 key per user), in stark contrast to other state-of-the-art schemes where the overhead grows linearly with the number of service levels. We perform a performance analysis of the proposed protocols using the building block protocol as a benchmark, which demonstrates their efficiency: each mobile user incurs a total energy cost of approximately 62.0 mJ per reporting round, with an average end-to-end aggregation latency of less than 10 milliseconds, demonstrating that the proposed protocols can be used in practical settings. While the proposed protocols rely on a trusted authority, a common assumption in existing privacy-preserving aggregation schemes, future work will explore decentralized key management to support fully trustless environments.	10.1109/TNSM.2026.3704409
Emilio Paolini, Andrea Pinto, Luca Valcarenghi, Flavio Esposito	Programmable In-Network Aggregation for Communication-Aware Federated Learning in 5G RANs	2026	Early Access	Modeling Timing Training	Federated Learning (FL) enables collaborative model training without sharing raw data, making it attractive for privacy-preserving applications at the wireless edge. However, when executed over real 5G networks, FL performance degrades due to uplink congestion, heterogeneous client capabilities, and intermittent connectivity. Most existing approaches attempt to mitigate these issues indirectly by optimizing clients (through adaptive participation, local training, or selection strategies) or by optimizing models (via pruning, quantization, or compression), but they ignore potential network bottlenecks. This paper introduces FLAG, an FL architecture that embeds innetwork aggregation directly into 5G gNodeBs, transforming the network into an active participant in the learning process. In particular, FLAG performs parameter aggregation at line rate within the 5G Service Data Adaptation Protocol layer and incorporates three mechanisms: Partial-Contribution Correction for loss-tolerant averaging, a timer-driven pipeline for real-time scheduling, and a deadline-based grouping strategy to mitigate stragglers. Experiments with realistic wireless emulation show that FLAG achieves up to 5.1× faster time-to-accuracy and maintains accuracy within 0.8% of a loss-free baseline, while reducing gNB-to-server bandwidth by aggregating pergNB rather than per-client. FLAG requires no modifications to clients or the parameter server, demonstrating how 5G-aware system design can make federated learning scalable, efficient, and resilient under real-world wireless conditions.	10.1109/TNSM.2026.3697723
Kunpeng Zheng, Huibin Zhang, Yongli Zhao, Yuan Cao, Wei Wang, Xin Li, Zhuangzhuang Ma, Lihan Zhao, Jie Zhang	Sun-Outage-Aware Topology Modeling and Adaptive Routing for Optical Satellite Networks	2026	Early Access	Sun Interrupters Joining processes	Optical satellite networks, supported by optical inter-satellite links (OISLs), provide reliable and low-latency optical connectivity. However, periodic and predictable sun outage events significantly compromise OISL availability, leading to frequent OISL interruptions and reduced network reliability. Existing routing algorithms often overlook the regularity of sun outage-induced interrupts and their differentiated impacts on services, resulting in degraded service performance. To address this challenge, this paper proposes a sun outage-enhanced time discretization OISL model and introduces a sun outage link-aware routing (SOLR) algorithm. By incorporating joint awareness of sun outage patterns and service requirements, SOLR employs an adaptive optimization mechanism to dynamically adjust routing decisions within temporal windows. Experimental results demonstrate that SOLR extends stable path durations by 39.9%, reduces interruption rates by 28.5%, and decreases blocking rates by 36.4%, significantly outperforming link-state-based routing algorithms. By effectively mitigating the impact of sun outages, SOLR ensures continuous optical service connections. This interruption-tolerant framework bridges network modeling and service provisioning, offering a robust solution for mission-critical service in optical satellite networks.	10.1109/TNSM.2026.3697856
Ishu Gupta, Ashutosh Kumar Singh	Statistical Analysis Driven Prediction Model for Malicious Entity Detection in Cloud Environment	2026	Early Access	Modeling Signal detection Clouds	Data sharing across distinct entities, including clouds, has become a necessity to enhance the performance of enterprises; however, it leads to data protection challenges. In this paper, a novel model aimed at data protection is presented when multiple untrusted parties are involved in the system. The proposed model enables secure data sharing and effective data distribution among the involved entities while minimizing the risk associated with data exposure. It enables the identification of malicious entities responsible for data leakage with high confidence. To this end, an efficient distribution strategy based on object and user selection, incorporating an operative access control mechanism, is proposed. Furthermore, algorithms are designed for the selection of data to be distributed among users. Experimental results demonstrate that the proposed model achieves significant improvements of 31%, 97%, and 64% in success rate, detection rate, and assessment rate, respectively, compared to prior works. Moreover, it reduces data leakage by up to 75% and lowers the error rate by up to 83% for malicious entity detection, while simultaneously enhancing detection performance and capability by up to 32% and 40%, respectively, over existing approaches.	10.1109/TNSM.2026.3704450
Josè Santos, Asser Tantawi, Pavlos Maniotis, Chen Wang, Olivier Tardieu, Tim Wauters, Filip De Turck	Sakkara: Intelligent Topology-Aware Scheduling for Kubernetes in the Age of AI	2026	Early Access	Topology Scheduling Schedules	The rapid growth of Artificial Intelligence (AI) workloads has introduced unprecedented challenges to modern cloud-native systems, particularly in Kubernetes (K8s)-based environments. These workloads often demand low-latency communication, high resource locality, and efficient utilization of heterogeneous hardware devices such as Graphics Processing Units (GPUs) and specialized accelerators. However, the existing scheduling mechanisms in K8s are typically unaware of the underlying physical topology, leading to performance degradation and inefficient resource usage. This paper presents Sakkara, a novel topology-aware scheduling framework designed to optimize the placement of AI workloads in K8s clusters. Sakkara incorporates a hierarchical model of the Data Center (DC), including nodes and racks, enabling flexible scheduling strategies that account for resource availability and risk-aware metrics that mitigate performance interference and constraint violations caused by topology-unaware placement. Sakkara extends existing scheduling logic in K8s with placement strategies that guide pod allocation using configurable topology constraints, aiming to minimize communication costs and maximize workload performance. We evaluated Sakkara on a representative AI workload, a distributed training application under different cluster configurations. Experimental results show that Sakkara improves job completion time, throughput, and memory utilization compared to available K8s schedulers, achieving improvements of up to 10%. Sakkara, available as open-source, offers a promising pathway toward topology-conscious orchestration of AI workloads in next-generation cloud environments.	10.1109/TNSM.2026.3703831
Mansoor Davoodi, Setareh Maghsudi	Efficient Resource Allocation under Adversary Attacks: A Decomposition-Based Approach	2026	Early Access	Resource management Optimization Modeling	We address the problem of allocating limited resources in a network under persistent yet statistically unknown adversarial attacks. Each node in the network may be degraded, but not fully disabled, depending on its available defensive resources. The objective is twofold: to minimize total system damage and to reduce cumulative resource allocation and transfer costs over time. We model this challenge as a bi-objective optimization problem and propose a decomposition-based solution that integrates chance-constrained programming with network flow optimization. The framework separates the problem into two interrelated subproblems: determining optimal node-level allocations across time slots, and computing efficient inter-node resource transfers. We theoretically prove the convergence of our method to the optimal solution that would be obtained with full statistical knowledge of the adversary. We further establish an O(√T log(nT)) regret bound, showing that the average per-round performance gap shrinks as O(1/√T). Extensive simulations demonstrate that our method efficiently learns the adversarial patterns and achieves substantial gains in minimizing both damage and operational costs, comparing three benchmark strategies under various parameter settings.	10.1109/TNSM.2026.3703620
Siya Xu, Ye Yu, Shaoyong Guo	F-CShard: A Fast Cross-Shard Consensus Protocol for the Large-Scale Sharing of Cultural Resources	2026	Early Access	Sharding Protocols Consensus protocol	Blockchain’s decentralization and immutability inherently ensure the privacy and transactional reliability of cultural resources. However, traditional global consensus mechanisms scale poorly with increasing data volume and transaction frequency. While sharding enhances blockchain scalability, current sharding-based implementations exhibit high latency and communication overhead during cross-shard transactions. In this paper, we propose F-CShard, a fast cross-shard consensus protocol that optimizes blockchain sharding and consensus for large-scale cultural resource sharing. F-CShard addresses two key challenges in existing systems: low transaction throughput and high cross-shard communication costs. Our solution incorporates four technical innovations. First, we construct a spatio-temporal correlation model based on historical transaction patterns and account geographical distribution to minimize cross-shard transactions. Second, we add a random-bit to optimize the Cuckoo Rule, thereby reducing the migratory frequency of nodes while improving system throughput and robustness. Third, we design a heartbeat-enhanced consensus protocol to decrease latency and communication overhead. Finally, we propose a cross-shard consensus protocol based on virtual accounts to simplify the processing of cross-shard transactions and ultimately improve the scalability and security of the system. Experimental results show that F-CShard outperforms X-Shard and LBF in terms of throughput and latency, and has near-linear scalability in high concurrency environments.	10.1109/TNSM.2026.3703588
Aziz Kord, Mary Gregg, M. Keith Forsyth, Julia L. Sharp, Jason B. Coder, Vu Le	Towards Improved Standards for Open RAN Interoperability: A Factor Screening Experiment	2026	Early Access	Information rates Open RAN Throughput	As Open Radio Access Networks (Open RAN) move toward large-scale deployment, the industry requires a transition from binary “pass/fail” conformance testing to a rigorous performance metrology that characterizes system stability in multi-vendor environments. This study introduces a statistical factor screening methodology, utilizing a Resolution V fractional factorial design, to isolate the key configuration parameters that govern interoperability in a commercial-grade 7.2x functional split testbed. By executing a 1,024-run automated experimental campaign, we identify critical non-linear "performance boundaries" where specific combinations of modulation coding, power control, and UE scaling cause functional decoupling between disaggregated O-DU and O-RU components. Our results demonstrate that O-RU interoperability is not a static state but a dynamic boundary of functional invariance. This work provides a mathematically grounded framework for Mobile Network Operators (MNOs) to prioritize high-impact factors in certification and badging, accelerating the maturity of secure, high-capacity Open RAN ecosystems.	10.1109/TNSM.2026.3703354
Ibirisol Fontes Ferreira, Eiji Oki	Forestall: A Prefetching Scheme for Domain Name System Resolver Cache Services	2026	Early Access	Prefetching Timing Servers	The domain name system (DNS) is crucial to accessing Internet services by playing an essential role in facilitating this process for Internet users. Still, it affects the quality of experience within the Internet service chain. This impact includes the role of the resolver component, which can negatively influence the final user experience when consuming services. Some studies have developed strategies to reduce resolution time within the DNS resolver ecosystem by incorporating components into users’ devices to trigger resolution in advance, changing DNS service and cache algorithm implementation, or utilizing a complex and expensive service architecture that is not scalable for local DNS resolvers in edge deployments. This paper proposes a dynamic prefetching scheme called Forestall to reduce misses, including those caused by expired domain translation data, and to improve the overall performance of the resolver cache component. We model the prefetching scheme for DNS resolvers using DNS transactional information. We define a prefetching advising routine that advises on possible domains by observing past request patterns. We introduce two prefetching routines for efficient domain tracking and advising. We introduce miss-based metrics to measure the efficiency of the prefetching scheme and the potential resource trade-off associated with its deployment. The numerical results indicate that the prefetching scheme improves the performance of the DNS resolver cache component compared to well-deployed prefetching solutions on the Internet. Forestall reduces the miss ratio by more than 50%, depending on the dataset. In a specific workload, Forestall’s results with adjusted parameter combinations yield a decrease in the miss ratio of more than 16%, accompanied by a reasonable increase in additional fetches of around 35%. In terms of service latency that users perceive, Forestall achieves a reduction varying between 20% and 49%.	10.1109/TNSM.2026.3704549
Arash Heidari, Jamal N. Al-Karaki	NOVA: A Self-Supervised Graph Framework for Real-Time Anomaly Detection in Internet of Vehicles	2026	Early Access	Context Internet of Vehicles Modeling	The Internet of Vehicles (IoV) enables cooperative driving and real-time Vehicle-to-Everything (V2X) communication but remains vulnerable to behavioral and structural anomalies due to its dynamic, decentralized nature. Existing deep learning methods either overlook topological inconsistencies or ignore communication feature fidelity, while random-walk sampling introduces contextual noise. In this paper, we propose Network Observation for Vehicular Anomalies (NOVA), a self-supervised graph-based framework that detects both behavioral and structural anomalies in IoV networks without labeled data. NOVA models vehicular communications as attributed graphs and employs intimacy-guided subgraph sampling to extract meaningful neighborhoods. A Graph Convolutional Network (GCN)–based generative module reconstructs node attributes to reveal behavioral deviations, while a contrastive module validates structural coherence through embedding comparisons of real and perturbed contexts. Their hybrid anomaly score enables accurate, scalable, and real-time detection of compromised nodes. Performance results show that NOVA achieves state-of-the-art performance (98.7% accuracy, 98.1% F1), real-time throughput (~4.7k events/s at 5k msg/s), and strong robustness (AUROC 0.99, AUPRC 0.98, FAR 0.05) with near-linear scalability (≤40 ms latency for 50k vehicles). By integrating generative and contrastive self-supervised learning with context-aware sampling, NOVA significantly enhances IoV security, reliability, and adaptability.	10.1109/TNSM.2026.3696324
Ryotaro Taniguchi, Takeru Inoue, Kazuya Anazawa, Eiji Oki	Terminal Shuffling for Twisted and Folded Clos Network Design: Guaranteeing Blocking Probability under Different Request Active Rates	2026	Early Access	Switches Probability Design methodology	Optical circuit switching (OCS) is being used in some data center networks due to its low power consumption, low latency, and high bandwidth. Previous research introduced a design model for a twisted and folded Clos network (TF-Clos) as a data center network to maximize the switching network size, i.e., the number of connected terminals, while guaranteeing the admissible blocking probability. The previous model assumes that the request active rates from all the terminals are identical. However, it is an overly conservative design when the active rates differ, resulting in a smaller switching network size than desired. This paper proposes a terminal-shuffling (TS) scheme for designing an OCS TF-Clos network with an admissible blocking probability guarantee, which supports different active rates. Each terminal can arbitrarily choose any leaf switch to connect, enhancing the flexibility of the network design to accommodate heterogeneous active rates across different terminals. A patch panel or direct termination by operators can wire optical fibers between the terminals and the leaf switches. We formulate a TS-based TF-Clos design problem to maximize the switching network size. We develop an approximation approach to find a feasible solution to the optimization problem. Numerical results demonstrate that the switching network size of the proposed TS scheme is larger than that of baseline schemes.	10.1109/TNSM.2026.3704894
Ehsan Etezadi, Farhad Arpanaei, Carlos Natalino, Erik Agrell, Paolo Monti, Marija Furdek	Fragmentation- and QoT-Aware RBMSA with Spectrum Defragmentation in Dynamic Multi-Band Elastic Optical Networks	2026	Early Access		Multi-band elastic optical networks (MB-EONs) transmit information in multiple bands to increase the available capacity. However, they suffer from quality of transmission (QoT) degradation caused by the inter-channel stimulated Raman scattering effect, which requires addressing through tailored resource assignment. Additionally, dynamically arriving and departing optical service requests generate spectrum fragmentation (SF), where spectrum resources become scattered into non-continuous chunks and aggravate service blocking ratio (SBR) even when the total available bandwidth is sufficient. To jointly address these challenges, we propose an SF- and QoT-aware algorithm for routing, band, modulation format, and spectrum assignment (RBMSA), along with proactive spectrum defragmentation (SD), referred to as SFQA-defrag. The algorithm considers SF metrics and QoT levels of available channels across multiple candidate paths to ensure that the QoT requirements are met while minimizing the SF. The SD process proactively reorganizes spectrum allocation to reduce fragmentation by consolidating the spectrum gaps, which leads to lower blocking of future requests. The SFQA-defrag algorithm is evaluated against benchmark algorithms that independently consider either QoT or SF in three reference backbone topologies. The results demonstrate that SFQA-defrag significantly reduces the SBR and SF compared to benchmarks, albeit with a slight increase in the average path length.	10.1109/TNSM.2026.3702377
Behrooz Farkiani, Fan Liu, Ke Yang, John DeHart, Jyoti Parwatikar, Patrick Crowley	Hermes: A General-Purpose Proxy-Enabled Networking Architecture	2026	Early Access		We introduce Hermes, a general-purpose networking architecture that aims to improve service delivery over the Internet. Hermes delegates networking responsibilities from applications and services to proxies and is designed as a portable, adaptable solution to four fundamental challenges of efficient service delivery over the Internet: end-to-end traffic management, backward compatibility, data-plane security and privacy models, and adaptable communication layers. The design centers on an overlay of reconfigurable proxies and HTTP tunneling and proxying techniques, utilizing assisting components to extend proxy functionality when needed. Through prototyping and emulation, we demonstrate that Hermes improves key performance metrics across multiple use cases: it provides backward compatibility through protocol translation and tunneling, improves reliability by delegating retry logic to proxies, enables unified policy-based Layer 3 routing across network segments, and serves as an efficient substrate for future architectures like NDN, facilitating their operation over the Internet. Beyond evaluating Hermes across various use cases, we measured the overhead of Hermes’ HTTP tunneling and proxying mechanisms and found it to be modest, typically under 2 ms per proxy pair traversal in an isolated collocated setup. Although the HTTP proxying and tunneling techniques used by Hermes increase single-connection processing overhead, we also show that, with up to 1,000 concurrent requests, proxies can amortize connection setup time and reduce end-to-end latency by utilizing connection pooling and multiplexing.	10.1109/TNSM.2026.3705327
Qing Chen, Hua Wu, Tian Tian, Anting Lu, Guang Cheng, Xiaoyan Hu	A Generalized Video Platform Identification Method over Obfuscated Encrypted Protocols in Real-world Networks	2026	Early Access		Despite platforms adopt encryption protocols such as TLS to protect user privacy, adversaries can still infer user preferences through platform identification attacks. To obtain enhanced privacy, a lot of users employ obfuscated encrypted protocols, such as encrypted proxies and virtual private networks. However, existing state-of-the-art platform identification methods are only effective in laboratory-closed networks. In real-world networks, their performance degrades significantly when confronted with unknown obfuscated encrypted protocols or dynamic transmission paths. In addition, asymmetric routing also substantially weakens their effectiveness, which is a prevalent scenario in real-world networks. To overcome these challenges, this paper introduces a generalized method for identifying encrypted video streams over obfuscated encrypted protocols. Our approach achieves this by designing protocol-agnostic and path-agnostic features through granular analysis of video transmission patterns. Specifically, we first extract the inherent transmission patterns from unidirectional flows. Subsequently, we derive robust statistical features from temporal and spatial dimensions, respectively. Finally, these features are used to train a machine-learning-based classifier. Our experimental results demonstrate that the proposed method achieves a classification accuracy exceeding 98% against both unknown obfuscated encrypted protocols and dynamic transmission paths. Compared with the state-of-the-art methods, our method requires only 15% of the storage and 74% of the computational time while delivering superior performance. These findings reveal significant privacy vulnerabilities in obfuscated encrypted protocols and underscore the urgent need for developing more advanced security mechanisms to provide users with stronger anonymity services.	10.1109/TNSM.2026.3705064