TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Manjuluri Anil Kumar, Balaprakasa Rao Killi, Eiji Oki	Generative Adversarial Networks Based Low-Rate Denial of Service Attack Detection and Mitigation in Software-Defined Networks	2025	Early Access	Protocols Prevention and mitigation Real-time systems	Low-rate Denial of Service (LDoS) attacks use short, regular bursts of traffic to exploit vulnerabilities in network protocols. They are a major threat to network security, especially in Software-Defined Networking (SDN) frameworks. These attacks are challenging to detect and mitigate because of their low traffic volume, making it impossible to distinguish them from normal traffic. We propose a real-time LDoS attack detection and mitigation framework that can protect SDN. The framework incorporates a detection module that uses a deep learning model, such as a Generative Adversarial Network (GAN), to identify the attack. An efficient mitigation module follows detection, employing mechanisms to identify and filter harmful flows in real time. Deploying the framework into SDN controllers guarantees compliance with OpenFlow standards, thereby avoiding the necessity for additional hardware. Experimental results demonstrate that the proposed system achieves a detection accuracy of over 99.98% with an average response time of 8.58 s, significantly outperforming traditional LDoS detection approaches. This study presents a scalable, real-time methodology to enhance SDN resilience against LDoS attacks.	10.1109/TNSM.2025.3625278
Soyinka Nath, Sujata Sengar, Shree Prakash Singh	Dynamic Re-Sizing of Co-Existing Optical-RF Networks for Enhancing Resilience	2025	Early Access	Radio frequency Resilience Optical fiber communication	In this paper, a novel dynamic network re-sizing strategy for enhancing the resilience of a static coexisting optical – RF (Co-OPRF) network is presented. The resilience of Co-OPRF network is evaluated in terms of outage probability which may serve as a metric of Quality-of Service (QoS). The deployment strategy permits the size of Co-OPRF network to be modified on a dynamic basis while supporting failing optical links. The proposed resilience governed dynamic network re-sizing (RGDRS) algorithm aims to improve the resilience of Co-OPRF network against fluctuating detrimental local optical channel conditions by taking into account the link outage, optical power investment and interference by the RF links. The algorithm for achieving dynamic network re-sizing relies on two tables which gives the necessary trade-offs between link outage and optical power. The presented work shows the calculation for obtaining these tables and corresponding impact on network performance.	10.1109/TNSM.2025.3597437
Junyu Li, Fei Zhou, Qi Xie, Nankun Mu, Yining Liu	Efficient Conditional Privacy-Preserving Heterogeneous Broadcast Signcryption for Collision Warning in VANETs	2025	Early Access	Security Privacy Encryption	Real-time performance is of utmost significance for communication in certain specific scenarios of vehicle-to-infrastructure (V2I) like collision warning systems. Vehicle-to-Everything (V2X) Broadcast signcryption is very suitable for these scenarios. However, current solutions prioritize generality, but may not be suitable for specialized communication situations, and many broadcast signcryption schemes suffer from low communication verification efficiency due to the sequence of decryption before verification. Moreover, most of existing broadcast signcryption schemes with single cryptosystem are not applicable for the heterogeneous networks of different Internet of Vehicles. To address these challenges, an efficient conditional privacy-preserving heterogeneous broadcast signcryption scheme(ECPHBS) is proposed, improving roadside unit verification to support batch verification of ciphertexts through a pre-authentication mechanism, and allowing vehicles to conduct secure communication with roadside units under the certificateless cryptosystem and the identity-based cryptosystem. Meanwhile, a tracking and revocation mechanism was introduced to achieve conditional privacy protection. Our formal security analysis demonstrates that the ECPHBS scheme formally achieves IND-CCA2 security under the CDH assumption and EUF-CMA security under the ECDL problem. Experimental results confirm its superior verification efficiency, especially with an increasing number of receiving RSUs, and a constant communication overhead. Furthermore, the RSU service capability analysis shows that our scheme enables RSUs to fully handle communication requests from approximately 500 vehicles within a 150-meter range, outperforming comparative schemes.	10.1109/TNSM.2025.3619109
Aitor Brazaola-Vicario, Vasileios Kouvakis, Stylianos E. Trevlakis, Alejandra Ruiz, Alexandros-Apostolos A. Boulogeorgos, Theodoros A. Tsiftsis, Dusit Niyato	High-Fidelity Coherent-One-Way QKD Simulation Framework for 6G Networks: Bridging Theory and Reality	2025	Early Access	Protocols Cows Security	Quantum key distribution (QKD) has emerged as a promising solution for guaranteeing information-theoretic security. Inspired by this, a great amount of research effort has been recently put on designing and testing QKD systems as well as articulating preliminary application scenarios. However, due to the considerable high-cost of QKD equipment, a lack of QKD communication system design tools, wide deployment of such systems and networks is challenging. Motivated by this, this paper introduces a QKD communication system design tool. First we articulate key operation elements of the QKD, and explain the feasibility and applicability of coherent-one-way (COW) QKD solutions. Next, we focus on documenting the corresponding simulation framework as well as defining the key performance metrics, i.e., quantum bit error rate (QBER), and secrecy key rate. To verify the accuracy of the simulation framework, we design and deploy a real-world QKD setup. We perform extensive experiments for three deployments of diverse transmission distance in the presence or absence of a QKD eavesdropper. The results reveal an acceptable match between simulations and experiments rendering the simulation framework a suitable tool for QKD communication system design.	10.1109/TNSM.2025.3619551
Jiaen Lv, Yifang Zhang, Shaowei Wang	VIDTRA: An Efficient and Resilient Video Preloading System	2025	Early Access	Videos Trajectory Codecs	With the increasing demand for video-based applications, the clarity and fluidity of videos have garnered widespread attention. Current video playback mechanisms either allocate resources to clients with good channel quality or maintain video playback continuity at low bit rates, affecting the user viewing experience. In this work, we propose a video preloading system, namely VIDTRA, which departs from the current paradigm and explores a resilient design scheme. The central insight in VIDTRA is to utilize network situation maps and user trajectory prediction to forecast the serving cell and received signal strength, thereby determining the duration of video preloading. Considering the predictability of public transport route trajectories, our system is primarily designed to enhance the video watching experience for users on public transport, which encompasses functionalities such as route clustering and the identification of users’ boarding and alighting. Using real-world data collected from user equipments, we thoroughly evaluate and demonstrate the efficacy of VIDTRA. Results from the experimental evaluations show that VIDTRA can precisely estimate the future signal strength received by users and initiate video preloading before they enter areas with weak signal quality, thus reducing the interruptions of the video while guaranteeing the high definition.	10.1109/TNSM.2025.3620295
Zheng Gao, Danfeng Sun, Jianyong Zhao, Huifeng Wu, Jia Wu	Cost-Minimized Data Edge Access Model for Digital Twin Using Cloud-Edge Collaboration	2025	Early Access	Data acquisition Cloud computing Digital twins	Industrial applications involving digital twins (e.g., behavior simulation) demand highly accurate, low-latency data, making real-time data acquisition critical. To meet performance demands, devices that do not support asynchronous communication need to acquire data at high frequency. In cloud-edge collaboration schemes, edge computing nodes typically acquire the data. However, high-frequency data acquisition and processing impose considerable costs, posing significant challenges for these resource-constrained nodes. To address this problem, we propose a model called Cost-minimized Data Edge Access (CDEA) that can dynamically minimize the edge costs while satisfying long-term performance requirements. CDEA quantifies data performance by decomposing the workflow of industrial systems into basic action units. These units are used to model data acquisition, data processing, data transmission, and cloud computing. Then, a cost minimization problem is formulated based on these components. To address irregular data changes and the general lack of available statistics on system’s network status, the framework incorporates Lyapunov optimization to transform the long-term guarantee over data performance into a series of instantaneous decision problems. Finally, a heuristic algorithm identifies the optimal data acquisition strategy. To validate CDEA’s effectiveness, we implemented it in two representative digital twin scenarios: cathode plate stripping and AGV transportation. Experimental results demonstrate that CDEA can indeed reduce both edge costs and cloud resources consumption while still ensuring high data performance.	10.1109/TNSM.2025.3621548
Mario Di Mauro	Performance Assessment of Multi-Class 5G Chains: A Non-Product-Form Queueing Networks Approach	2025	Early Access	Delays 5G mobile communication Queueing analysis	This work presents a performance assessment of 5G Service Function Chains (SFCs) by examining and comparing two architectural models. The first is the Mono chain model, which relies on a single path for data processing through a series of 5G nodes, ensuring straightforward and streamlined service delivery. The second is the Poly (or sliced) chain model, which leverages multiple paths for data flow, enhancing load balancing and resource distribution across nodes to improve network resilience. To evaluate the performance of these models, we introduce a performance indicator that captures two critical stages: the time required for user registration to the 5G infrastructure and the time needed for Protocol Data Unit (PDU) session establishment. From a performance standpoint, these stages are deemed crucial by the European Telecommunications Standards Institute (ETSI), as they can adversely affect both objective and subjective network parameters. Using a non-product-form queueing network approach, we develop an algorithm named ChainPerfEval, which accurately estimates the proposed performance indicator. This approach outperforms standard queueing network models, where the exponential assumption of inter-arrival and/or service times may lead to an inaccurate estimation of the performance indicator. An extensive experimental campaign is conducted using an Open5GS testbed to simulate real-world traffic scenarios, categorizing 5G flows into three priority classes: gold (high priority), silver (moderate priority), and bronze (low priority). The results provide significant insights into the trade-offs between the Mono and Poly chain models, particularly in terms of resource allocation strategies and their impact on SFC performance. Ultimately, this comprehensive analysis offers valuable and actionable recommendations for network operators seeking to optimize service delivery in multi-class 5G environments, ensuring enhanced user experience and efficient resource utilization.	10.1109/TNSM.2025.3588304
Seyed Soheil Johari, Massimo Tornatore, Nashid Shahriar, Raouf Boutaba, Aladdin Saleh	Active Learning for Transformer-Based Fault Diagnosis in 5G and Beyond Mobile Networks	2025	Early Access	Transformers Fault diagnosis Data models	As 5G and beyond mobile networks evolve, their increasing complexity necessitates advanced, automated, and datadriven fault diagnosis methods. While traditional data-driven methods falter with modern network complexities, Transformer models have proven highly effective for fault diagnosis through their efficient processing of sequential and time-series data. However, these Transformer-based methods demand substantial labeled data, which is costly to obtain. To address the lack of labeled data, we propose a novel active learning (AL) approach designed for Transformer-based fault diagnosis, tailored to the time-series nature of network data. AL reduces the need for extensive labeled datasets by iteratively selecting the most informative samples for labeling. Our AL method exploits the interpretability of Transformers, using their attention weights to create dependency graphs that represent processing patterns of data points. By formulating a one-class novelty detection problem on these graphs, we identify whether an unlabeled sample is processed differently from labeled ones in the previous training cycle and designate novel samples for expert annotation. Extensive experiments on real-world datasets show that our AL method achieves higher F1-scores than state-of-the-art AL algorithms with 50% fewer labeled samples and surpasses existing methods by up to 150% in identifying samples related to unseen fault types.	10.1109/TNSM.2025.3622149
Jingchao Tan, Tiancheng Zhang, Cheng Zhang, Chenyang Wang, Chao Qiu, Xiaofei Wang, Mohsen Guizani	Delay-Aware and Energy-Efficient Integrated Optimization System for 5G Networks	2025	Early Access	5G mobile communication Delays Energy efficiency	To meet the demands of high-capacity and low-delay services, Fifth Generation (5G) Base Stations (BSs) are typically deployed in ultra-dense configurations, especially in urban areas. While this densification enhances coverage and service quality, it also leads to substantially increased energy consumption. However, the dense deployment pattern makes BS workloads more responsive to the spatiotemporal variations in user behavior, offering opportunities for energy-saving strategies that dynamically adjust BS operation states. In this context, we propose a Delay-aware and Energy-efficient Integrated Optimization System (DEIS) based on Deep Reinforcement Learning (DRL), which jointly optimizes energy consumption and network delay while maintaining user satisfaction. DEIS leverages a real-world dataset collected from operational 5G BSs provided by partner network operators, containing both BS deployment data and high-volume user request logs. Extensive simulations demonstrate that DEIS can achieve a 41% reduction in energy consumption while ensuring reliable delay performance.	10.1109/TNSM.2025.3623778
Akhila Rao, Magnus Boman	Self-Supervised Pretraining for User Performance Prediction Under Scarce Data Conditions	2025	Early Access	Generators Training Self-supervised learning	Predicting user performance at the base station in telecom networks is a critical task that can significantly benefit from advanced machine learning techniques. However, labeled data for user performance are scarce and costly to collect, while unlabeled data consisting of base station metrics, are more readily accessible. Self-supervised learning provides a means to leverage this unlabeled data, and has seen remarkable success in the domains of computer vision and natural language processing, with unstructured data. Recently, these methods have been adapted to structured data as well, making them particularly relevant to the telecom domain. We apply self-supervised learning to predict user performance in telecom networks. Our results demonstrate that even with simple self-supervised approaches, the percentage of variance of the target values explained by the model, in low-labeled scenarios (e.g., only 100 labeled samples) can be improved fourfold, from 15% to 60%. Moreover, to promote reproducibility and further research in the domain, we open-source a dataset creation framework and a specific dataset created from it that captures scenarios that have been deemed to be challenging for future networks.	10.1109/TNSM.2025.3622892
Abdurrahman Elmaghbub, Bechir Hamdaoui	HEEDFUL: Leveraging Sequential Transfer Learning for Robust WiFi Device Fingerprinting Amid Hardware Warm-Up Effects	2025	Early Access	Fingerprint recognition Radio frequency Hardware	Deep Learning-based RF fingerprinting approaches struggle to perform well in cross-domain scenarios, particularly during hardware warm-up. This often-overlooked vulnerability has been jeopardizing their reliability and their adoption in practical settings. To address this critical gap, in this work, we first dive deep into the anatomy of RF fingerprints, revealing insights into the temporal fingerprinting variations during and post hardware stabilization. Introducing HEEDFUL, a novel framework harnessing sequential transfer learning and targeted impairment estimation, we then address these challenges with remarkable consistency, eliminating blind spots even during challenging warm-up phases. Our evaluation showcases HEEDFULs efficacy, achieving remarkable classification accuracies of up to 96% during the initial device operation intervals–far surpassing traditional models. Furthermore, cross-day and crossprotocol assessments confirm HEEDFUL’s superiority, achieving and maintaining high accuracy during both the stable and initial warm-up phases when tested on WiFi signals. Additionally, we release WiFi type B and N RF fingerprint datasets that, for the first time, incorporate both the time-domain representation and real hardware impairments of the frames. This underscores the importance of leveraging hardware impairment data, enabling a deeper understanding of fingerprints and facilitating the development of more robust RF fingerprinting solutions.	10.1109/TNSM.2025.3624126
Giovanni Simone Sticca, Memedhe Ibrahimi, Francesco Musumeci, Nicola Di Cicco, Massimo Tornatore	Hollow-Core Fibers for Latency-Constrained and Low-Cost Edge Data Center Networks	2025	Early Access	Optical fiber networks Costs Optical fiber communication	Recent advancements in Hollow Core Fibers (HCF) production are paving the way toward new ground-breaking opportunities of HCF for 6G-and-beyond applications. While Standard Single-Mode Fibers (SSMF) have been the go-to solution in optical communications for the past 50 years, HCF is expected to be a turning point in how next-generation optical networks are planned and designed. Compared to SSMF, in which the optical signal is transmitted in a silica core, in HCF, the optical signal is transmitted in a hollow, i.e., air, core, significantly reducing latency (by 30%), while also decreasing attenuation (as low as 0.11 dB/km) and non-linearities. In this study, we investigate the optimal placement of HCF in latency-constrained optical networks to minimize the number of edge Data Centers (edgeDCs), while also ensuring physical-layer validation. Given the optimized placement of HCF and edgeDCs, we minimize the overall network cost in terms of transponders (TXPs) and Wavelength Selective Switches (WSSes) by optimizing the type, number, and transmission mode of TXPs, and the type and number of WSSes. We develop a Mixed Integer Nonlinear Programming (MINLP) model and a Genetic Algorithm (GA) to solve these problems. We validate the GA against the MINLP model in four synthetically generated topologies and perform extensive numerical evaluations in a realistic 25-node metro aggregation topology and a 22-node national topology. We show that by upgrading 25% of the links to HCF, we can significantly reduce the number of edgeDCs by up to 40%, while also reducing network equipment cost by up to 38%, compared to an SSMF-only network.	10.1109/TNSM.2025.3625391
Hernani D. Chantre, Nelson L. S. da Fonseca	Cost Analysis of VNF Distributions in 5G MEC-Based Networks With Protection Scheme	2025	Early Access	Costs Protection 5G mobile communication	This paper addresses the optimal placement of Multi-Access Edge Computing (MEC) nodes in 5G networks, aiming to meet stringent performance requirements while minimizing cost. It explores the impact of various Virtual Network Function (VNF) distribution strategies on overall network cost, specifically examining the 1:1, 1:N, and 1:N:K protection schemes. To tackle the MEC location problem, bi-objective nonlinear mathematical models are employed for exact solutions in small-scale networks, while the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied for larger networks. The results reveal that VNF distribution can significantly escalate network costs, with fully distributed VNFs incurring the highest expenses. However, the enhanced protection scheme demonstrates improved cost-efficiency. These findings highlight the critical role of strategic MEC placement and intelligent resource allocation in building scalable, resilient, cost-effective 5G infrastructures.	10.1109/TNSM.2025.3619023
Anurag Dutta, Sangita Roy, Rajat Subhra Chakraborty	RISK-4-Auto: Residually Interconnected and Superimposed Kolmogorov-Arnold Networks for Automotive Network Traffic Classification	2025	Early Access	Telecommunication traffic Accuracy Visualization	In modern automobiles, a Controller Area Network (CAN) bus facilitates communication among all electronic control units for critical safety functions, including steering, braking, and fuel injection. However, due to the lack of security features, it may be vulnerable to malicious bus traffic-based attacks that cause the automobile to malfunction. Such malicious bus traffic can be the result of either external fabricated messages or direct injection through the on-board diagnostic port, highlighting the need for an effective intrusion detection system to efficiently identify suspicious network flows and potential intrusions. This work introduces Residually Interconnected and Superimposed Kolmogorov-Arnold Networks (RISK-4-Auto), a set of four deep neural network architectures for intrusion detection targeting in-vehicle network traffic classification. RISK-4-Auto models, when applied on three hexadecimally identifiable sequence-based open-source datasets (collected through direct injection in the on-board diagnostic port), outperform six state-of-the-art vehicular network intrusion detection systems (as per their accuracies) by ≈1.0163% for all-class classification and ≈2.5535% on focused (single-class) malicious flow detection. Additionally, RISK-4-Auto enjoys a significantly lower overhead than existing state-of-the-art models, and is suitable for real-time deployment in resource-constrained automotive environments.	10.1109/TNSM.2025.3625404
Ning Zhao, Dongke Zhao, Huiyan Zhang, Yongchao Liu, Liang Zhang	Resilient Dynamic Event-Triggered Fuzzy Tracking Control for Nonlinear Systems Under Hybrid Attacks	2025	Early Access	Event detection Fuzzy systems Denial-of-service attack	This article investigates the issue of event-triggered tracking control for Takagi–Sugeno fuzzy systems subject to hybrid attacks. First, the deception attacks occurring on the feedback channel are considered using a Bernoulli process, in which an attacker injects state-dependent malicious signals. Next, the minimal ‘silent’ and maximal ‘active’ periods are defined to describe the duration of aperiodic denial-of-service (DoS) attacks. To take advantage of communication bandwidth and resist DoS attacks, a sampled data-based resilient dynamic event-triggered strategy is designed. Then, an event-based fuzzy tracking controller is designed to guarantee the stability of error system under hybrid attacks. Subsequently, sufficient conditions for the stability analysis are proposed by utilizing a fuzzy-basis-dependent Lyapunov-Krasovskii functional. Meanwhile, the control gains and event-triggering parameters are co-designed by applying linear matrix inequalities. Furthermore, the proposed method is extended to address the tracking control problem of multi-agent systems. Finally, the feasibility of the presented approach is validated by two examples.	10.1109/TNSM.2025.3625395
Samayveer Singh, Aruna Malik, Vikas Tyagi, Rajeev Kumar, Neeraj Kumar, Shakir Khan, Mohd Fazil	Dynamic Energy Management in Heterogeneous Sensor Networks Using Hippopotamus-Inspired Clustering	2025	Early Access	Wireless sensor networks Clustering algorithms Optimization	The rapid expansion of smart technologies and IoT has made Wireless Sensor Networks (WSNs) essential for real-time applications such as industrial automation, environmental monitoring, and healthcare. Despite advances in sensor node technology, energy efficiency remains a key challenge due to the limited battery life of nodes, which often operate in remote environments. Effective clustering, where Cluster Heads (CHs) manage data aggregation and transmission, is crucial for optimizing energy use. Motivated from the above, in this paper, we introduce a novel metaheuristic approach called Hippopotamus Optimization-Based Cluster Head Selection (HO-CHS), designed to enhance CH selection by dynamically considering factors such as residual energy, node location, and network topology. Inspired by natural behaviors, HO-CHS effectively balances energy loads, reduces communication distances, and boosts network scalability and reliability. The proposed scheme achieves a 35% increase in network lifetime and a 40% improvement in stability period in comparison to the other existing schemes in literature. Simulation results demonstrate that HO-CHS significantly reduces energy consumption and enhances data transmission efficiency, making it ideal for IoT-enabled consumer electronics networks requiring consistent performance and energy conservation.	10.1109/TNSM.2025.3618766
Fabian Graf, David Pauli, Michael Villnow, Thomas Watteyne	Management of 6TiSCH Networks Using CORECONF: A Clustering Use Case	2025	Early Access	Protocols IEEE 802.15 Standard Reliability	Industrial low-power wireless sensor networks demand high reliability and adaptability to cope with dynamic environments and evolving network requirements. While the 6TiSCH protocol stack provides reliable low-power communication, the CoAP Management Interface (CORECONF) for runtime management remains underutilized. In this work, we implement CORECONF and introduce clustering as a practical use case. We implement a cluster formation mechanism aligned with the Routing Protocol for Low-Power and Lossy Networks (RPL) and adjust the TSCH channel-hopping sequence within the established clusters. Two use cases are presented. First, CORECONF is used to mitigate external Wi-Fi interference by forming a cluster with a modified channel set that excludes the affected frequencies. Second, CORECONF is employed to create a priority cluster of sensor nodes that require higher reliability and reduced latency, such as those monitoring critical infrastructure in industrial settings. Simulation results show significant improvements in latency, while practical experiments demonstrate a reduction in overall network charge consumption from approximately 50 mC per hour to 23 mC per hour, by adapting the channel set within the interference-affected cluster.	10.1109/TNSM.2025.3627112
Andrea Detti, Alessandro Favale	Cost-Effective Cloud-Edge Elasticity for Microservice Applications	2025	Early Access	Microservice architectures Cloud computing Data centers	Microservice applications, composed of independent containerized components, are well-suited for hybrid cloud–edge deployments. In such environments, placing microservices at the edge can reduce latency but incurs significantly higher resource costs compared to the cloud. This paper addresses the problem of selectively replicating microservices at the edge to ensure that the average user-perceived delay remains below a configurable threshold, while minimizing total deployment cost under a pay-per-use model for CPU, memory, and network traffic. We propose a greedy placement strategy based on a novel analytical model of delay and cost, tailored to synchronous request/response applications in cloud–edge topologies with elastic resource availability. The algorithm leverages telemetry and load balancing capabilities provided by service mesh frameworks to guide edge replication decisions. The proposed approach is implemented in an open-source Kubernetes controller, the Geographical Microservice Autoplacer (GMA), which integrates seamlessly with Istio and Horizontal Pod Autoscalers. GMA automates telemetry collection, cost-aware decision making, and geographically distributed placement. Its effectiveness is demonstrated through simulation and real testbed deployment.	10.1109/TNSM.2025.3627155
Shaocong Feng, Baojiang Cui, Junsong Fu, Meiyi Jiang, Shengjia Chang	Adaptive Target Device Model Identification Attack in 5G Mobile Network	2025	Early Access	Object recognition Adaptation models 5G mobile communication	Enhanced system capacity is one of 5G goals. This will lead to massive heterogeneous devices in mobile networks. Mobile devices that lack basic security capability have chipset, operating system or software vulnerability. Attackers can perform Advanced Persistent Threat (APT) Attack for specific device models. In this paper, we propose an Adaptive Target Device Model Identification Attack (ATDMIA) that provides the prior knowledge for exploiting baseband vulnerability to perform targeted attacks. We discovered Globally Unique Temporary Identity (GUTI) Reuse in Evolved Packet Switching Fallback (EPSFB) and Leakage of User Equipment (UE) Capability vulnerability. Utilizing silent calls, an attacker can capture and correlate the signaling traces of the target subscriber from air interface within a specific geographic area. In addition, we design an adaptive identification algorithm which utilizes both invisible and explicit features of UE capability information to efficiently identify device models. We conducted an empirical study using 105 commercial devices, including network configuration, attack efficiency, time overhead and open-world evaluation experiments. The experimental results showed that ATDMIA can accurately correlate the EPSFB signaling traces of target victim and effectively identify the device model or manufacturer.	10.1109/TNSM.2025.3626804
Zhengge Yi, Tengyao Li, Meng Zhang, Xiaoyun Yuan, Shaoyong Du, Xiangyang Luo	An Efficient Website Fingerprinting for New Websites Emerging Based on Incremental Learning	2025	Early Access	Incremental learning Fingerprint recognition Data models	Website fingerprinting attacks leverage encrypted traffic features to identify specific services accessed by users within anonymity networks such as Tor. Although existing WF methods achieve high accuracy on static datasets using deep learning techniques, they struggle in dynamic environments where anonymous websites continually evolve. These methods typically require full retraining on composite datasets, resulting in substantial computational and storage burdens, and are particularly vulnerable to classification bias caused by data imbalance and concept drift. To address these challenges, we propose EIL-WF, a dynamic WF framework based on incremental learning that enables efficient adaptation to newly emerging websites without the need for full retraining. EIL-WF incrementally trains lightweight, independent classifiers for new website classes and integrates them through classifier normalization and energy alignment strategies grounded in energy-based model theory, thereby constructing a unified and robust classification model. Comprehensive experiments on two public Tor traffic datasets demonstrate that EIL-WF outperforms existing incremental learning methods by 6.2%–20.2% in identifying new websites and reduces catastrophic forgetting by 5.4%–20%. Notably, EIL-WF exhibits strong resilience against data imbalance and concept drift, maintaining stable classification performance across evolving distributions. Furthermore,EIL-WF decreases training time during model updates by 2–3 orders of magnitude, demonstrating substantial advantages over conventional full retraining paradigms.	10.1109/TNSM.2025.3627441