TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Abdurrahman Elmaghbub, Bechir Hamdaoui	HEEDFUL: Leveraging Sequential Transfer Learning for Robust WiFi Device Fingerprinting Amid Hardware Warm-Up Effects	2026	Vol. 23, Issue	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 HEEDFUL‘s efficacy, achieving remarkable classification accuracies of up to 96% during the initial device operation intervals—far surpassing traditional models. Furthermore, cross-day and cross-protocol 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
Anna Karanika, Rui Yang, Xiaojuan Ma, Jiangran Wang, Shalni Sundram, Indranil Gupta	There is More Control in Egalitarian Edge IoT Meshes	2026	Vol. 23, Issue	Internet of Things Smart devices Intelligent sensors	While mesh networking for edge settings (e.g., smart buildings, farms, battlefields, etc.) has received much attention, the layer of control over such meshes remains largely centralized and cloud-based. This paper focuses on applications with commonplace sense-trigger-actuate (STA) workloads—like the abstraction of routines popular now in smart homes, but applied to larger-scale edge IoT deployments. We present CoMesh, which tackles the challenge of building a decentralized mesh-based control plane for local, non-cloud, and hubless management of sense-trigger-actuate applications. CoMesh builds atop an abstraction called the coterie, which spreads STA load in a fine-grained way both across space and across time. A coterie uses a novel combination of techniques such as zero-message-exchange protocols (for fast proactive member selection), quorum-based agreement, and locality-sensitive hashing. We analyze and theoretically prove safety and liveness properties of CoMesh. Our evaluation with both a Raspberry Pi-4 deployment and larger-scale simulations, using real building maps and real routine workloads, shows that CoMesh is load-balanced, fast, fault-tolerant, and scalable.	10.1109/TNSM.2025.3608796
Abdalla Hussein, Patrick Mitran, Catherine Rosenberg	Spectrum and RAN Sharing: How to Avoid Cross-Subsidization While Taking Full Advantage of Massive MU-MIMO?	2026	Vol. 23, Issue	Benchmark testing Resource management Graph neural networks	Motivated by the need to use spectrum more efficiently, this paper investigates fine grained spectrum sharing (FGSS) in Multi-User massive MIMO (MU-mMIMO) systems where a neutral host enables users from different operators to share the same resource blocks. To be accepted by operators, FGSS must i) guarantee isolation so that the load of one operator does not impact the performance of another, and ii) avoid cross-subsidization whereby one operator gains more from sharing than another. We first formulate and solve an offline problem to assess the potential performance gains of FGSS with respect to the static spectrum sharing case, where operators have fixed separate sub-bands, and find that the gains can be significant, motivating the development for online solutions for FGSS. Transitioning from an offline to an online study presents unique challenges, including the lack of apriori knowledge regarding the performance of the fixed sharing case that is required to ensure isolation and cross-subsidization avoidance. We overcome these challenges and propose an online algorithm that is fast and significantly outperforms the static case. The main finding is that FGSS for a MU-mMIMO downlink system is doable in a way that is “safe” to operators and brings large gains in spectrum efficiency (e.g., for 4 operators, a gain above 60% is seen in many cases).	10.1109/TNSM.2025.3612425
Indukuri Mani Varma, Neetesh Kumar	Blockchain-Based SDN-Enabled Lightweight Authentication Protocol for IoV Using zk-SNARK	2026	Vol. 23, Issue	Authentication Protocols Handover	Software-defined networking (SDN), an emerging networking paradigm, has been realized in the Internet of Vehicles to effectively manage the dynamic nature of vehicles and provide diverse vehicle-to-everything (V2X) services and security. Nevertheless, the lack of authentication mechanisms and malicious behavior of vehicle users has seriously threatened security and privacy concerns in the SDN-based vehicular network (SDVN). Furthermore, in a distributed SDVN, the SDN controllers (SDNCs) are prone to single point of failure (SPoF) due to various potential attack vectors. Such attacks can result in the loss of periodically exchanged vehicular information among SDNCs. To address the aforementioned issues, a novel privacy-preserving lightweight zk-SNARK-based authentication protocol in blockchain-enabled distributed SDVN is presented. To address the SPoF of SDNCs, the proposed network model removes the dependency of a controller on neighboring SDNCs as the authenticated information is validated and stored in the blockchain. As an integral part of the protocol, the vehicles are authenticated by providing succinct and constant size proofs during authentication across SDVN domains. The security and simulation performance analyses confirm the superior performance of the proposed protocol, surpassing state-of-the-art schemes in terms of authentication latency, computation time, and energy efficiency. The proposed protocol has achieved notable improvements of more than 55% in all the metrics considered during the protocol simulation when compared with other protocols.	10.1109/TNSM.2025.3613415
Remi Hendriks, Mattijs Jonker, Roland van Rijswijk-Deij, Raffaele Sommese	Load-Balancing Versus Anycast: A First Look at Operational Challenges	2026	Vol. 23, Issue	Routing Internet Routing protocols	Load Balancing (LB) is a routing strategy that increases performance by distributing traffic over multiple outgoing paths. In this work, we introduce a novel methodology to detect the influence of LB on anycast routing, which can be used by operators to detect networks that experience anycast site flipping, where traffic from a single client reaches multiple anycast sites. We use our methodology to measure the effects of LB-behavior on anycast routing at a global scale, covering both IPv4 and IPv6. Our results show that LB-induced anycast site flipping is widespread. The results also show our method can detect LB implementations on the global Internet, including detection and classification of Points-of-Presence (PoP) and egress selection techniques deployed by hypergiants, cloud providers, and network operators. We observe LB-induced site flipping directs distinct flows to different anycast sites with significant latency inflation. In cases with two paths between an anycast instance and a load-balanced destination, we observe an average RTT difference of 30 ms with 8% of load-balanced destinations seeing RTT differences of over 100 ms. Being able to detect these cases can help anycast operators significantly improve their service for affected clients.	10.1109/TNSM.2025.3636785
Muhammad Ashar Tariq, Malik Muhammad Saad, Dongkyun Kim	DDPG-Based Resource Management in Network Slicing for 5G-Advanced V2X Services	2026	Vol. 23, Issue	Resource management Quality of service Network slicing	The evolution of 5G technology towards 5G-Advanced has introduced advanced vehicular applications with stringent Quality-of-Service (QoS) requirements. Addressing these demands necessitates intelligent resource management within the standard 3GPP network slicing framework. This paper proposes a novel resource management scheme leveraging a Deep Deterministic Policy Gradient (DDPG) algorithm implemented in the Network Slice Subnet Management Function (NSSMF). The scheme dynamically allocates resources to network slices based on real-time traffic demands while maintaining compatibility with existing infrastructure, ensuring cost-effectiveness. The proposed framework features a two-level architecture: the gNodeB optimizes slice-level resource allocation at the upper level, and vehicles reserve resources dynamically at the lower level using the 3GPP Semi-Persistent Scheduling (SPS) mechanism. Evaluation in a realistic, trace-based vehicular environment demonstrates the scheme’s superiority over traditional approaches, achieving higher Packet Delivery Ratio (PDR), improved Spectral Efficiency (SE), and adaptability under varying vehicular densities. These results underscore the potential of the proposed solution in meeting the QoS demands of critical 5G-Advanced vehicular applications.	10.1109/TNSM.2025.3629529
Junbin Liang, Wenkang Li, Victor C. M. Leung	Stateful Virtual Network Function Decomposition and Deployment With Reliability Guarantee in Edge Networks	2026	Vol. 23, Issue	Reliability Costs Synchronization	Edge Networks (ENs) are emerging networks that enable deploying multiple virtual network functions (VNFs) on resource-limited edge servers to provide users with tailored virtual network services. Decomposing a single VNF into multiple thinner replicas can enhance service reliability while inevitably incurring additional computing capacity consumption (e.g., operating system overhead caused by instantiating more replicas), which increases with the number of decomposed replicas. Moreover, redundant backup replicas can be deployed near the replicas to enhance the reliability further. However, the stateful nature of VNFs requires state synchronization among replicas and between replicas and backup replicas, resulting in additional communication traffic. In this paper, we consider a joint strategy for the decomposition and deployment of stateful VNFs with the goal of minimizing total cost while meeting users’ reliability requirements. The total cost includes the computing cost for instantiating replicas and backup replicas, the additional consumption of computing capacity due to VNF decomposition, and the communication cost for routing traffic among users, replicas, and backup replicas. We first formulate the cost minimization problem as an integer nonlinear program and prove that it is NP-hard. Then, we propose an online two-stage scheme to solve this problem, where the first stage is a VNF decomposition algorithm, and the second stage is a deployment algorithm based on deep reinforcement learning (DRL). The former effectively reduces computing cost by iteratively adjusting the number of replicas and backup replicas, while aiding the latter to adaptively minimize communication cost. Extensive experiments demonstrate that our scheme is promising compared to existing state-of-the-art methods.	10.1109/TNSM.2025.3616185
Jing Li, Mohd Shahizan Othman, Xugang Ying, Dina S. M. Hassan, Hewan Chen, Lizawati Mi Yusuf	Adaptive NetFlow IIoT Intrusion Detection With Deep Transfer Learning, Genetic Optimization, and Ensemble Methods for Network Management	2026	Vol. 23, Issue	Transfer learning Accuracy Security	The growing demands of Industry 5.0 necessitate resilient Internet of Things (IoT) networks, which are increasingly susceptible to sophisticated cyber threats. While advancements in intrusion detection systems (IDS) have improved attack detection, addressing the complexity of multi-class attack scenarios and managing minority threats remains challenging. This study proposes NFIIoT-DTL-IDS, an adaptive IoT IDS for smart network management using NetFlow and IIoT data, driven by deep transfer learning and enhanced with genetic algorithm (GA) optimization. Our framework leverages pre-trained CNN models to convert data into images, enabling effective classification. GA optimizes hyperparameters to improve model flexibility and performance, while a soft voting ensemble ensures robust aggregation of predictions. The proposed IDS achieves 100% classification accuracy among 5, 10, and 19 distinct attack classes of three IoT datasets, including two NetFlow datasets (NF-TON-IoTv2 and NF-BoT-IoTv2) and one industrial-based dataset (X-IIoTID), detecting threats such as DDoS, ransomware, and theft in common IoT cyberattacks, as well as MQTT subscription and crypto-ransomware attacks in industrial IoT scenarios. Additional experiments demonstrate the effectiveness of the proposed method, which exceeds the classification performance results of three baseline models, including LSTM, Transformer, and 3D CNN, by more than 37.2%, 0.25%, and 1.25%, respectively, in the maximum among the three datasets. Finally, experimental results demonstrate that NFIIoT-DTL-IDS outperforms recent state-of-the-art solutions in terms of multiclassification accuracy. This contribution advances adaptive management frameworks for IoT security, offering scalable and high-performance solutions for intrusion detection in modern network management.	10.1109/TNSM.2025.3617765
Zhe Tian, Yiyi Zhang, Peng Guo, Mi Yan, Chi Zhang	Heterogeneous Radios Meet Intelligence: DRL-Based Delay Minimization in Multi-Hop Wireless Networks	2026	Vol. 23, Issue	Radio frequency Optimization Delays	Wireless Multi-Hop Networks (WMNs) have been widely applied in many industrial monitoring applications, where typically nodes deployed on machines sample the data periodically and the data then needs to be gathered to the sink node in time. Due to the complicated industrial environment, the channel capacity between neighboring nodes differs greatly from each other. This leads to a typical dilemma of radio communication module selection, i.e., choosing the wide-bandwidth module with short communication range or the long-range module with low bandwidth. Guaranteeing both the connectivity of the network and high network throughput is not easy. To address this issue, we equip the nodes with heterogeneous radio modules, leveraging their advantages to ensure both long-distance and high-bandwidth communication capabilities. The problem of efficiently scheduling the links between the nodes with multiple heterogeneous RF modules to minimize the data gathering delay has not yet been explored in existing research. In this paper, we propose a low-latency data collection method based on Deep Reinforcement Learning, with which nodes can autonomously appropriately schedule and assign their heterogeneous RF modules to their transmission/reception targets based on the current amount of their local data as well as that of their neighbors’ data. Extensive simulations are conducted, and the results show that the proposed method can achieve 10%-25% latency reduction compared to existing multi-radio approaches.	10.1109/TNSM.2025.3617954
Hernani D. Chantre, Nelson Luis Saldanha da Fonseca	Cost Analysis of VNF Distributions in 5G MEC-Based Networks With Protection Scheme	2026	Vol. 23, Issue	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
Junyu Li, Fei Zhou, Qi Xie, Nankun Mu, Yining Liu	Efficient Conditional Privacy-Preserving Heterogeneous Broadcast Signcryption for Collision Warning in VANETs	2026	Vol. 23, Issue	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	2026	Vol. 23, Issue	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
Dezhang Kong, Xiang Chen, Hang Lin, Zhengyan Zhou, Yi Shen, Hongyan Liu, Qiumei Cheng, Xuan Liu, Dong Zhang, Chunming Wu, Muhammad Khurram Khan	Toward Security-Enhanced In-Band Network Telemetry in Programmable Networks	2026	Vol. 23, Issue	Security Metadata Telemetry	In-band Network Telemetry (INT) is a widely used monitoring framework in modern large-scale networks. It provides packet-level visibility into network conditions by inserting telemetry data into packets, enabling unprecedented fine-grained network management. However, this mechanism also introduces new vulnerabilities that malicious attackers can exploit. In this paper, we present eight In-band Network Telemetry Manipulation Attacks that take advantage of INT’s weakness, demonstrating that attackers can cause severe damage with little effort by manipulating INT packets. To address this issue, we designed SecureINT, a security-enhanced INT prototype that provides encryption and integrity verification for INT packets. Specifically, SecureINT deploys Even-Mansour and SipHash for confidentiality and integrity, respectively. It also uses a zero-delay rotation mechanism, which enables administrators to dynamically change the version of the deployed Even-Mansour/SipHash running on programmable switches without the need to re-install new programs. In this way, SecureINT can provide lasting security for INT packets using the limited resources of programmable switches. According to the experiments, SecureINT can be deployed on programmable switches using a single pipeline. Besides, the overhead of the rotation mechanism running on the control plane is still minimal.	10.1109/TNSM.2024.3504563
Seyed Soheil Johari, Massimo Tornatore, Nashid Shahriar, Raouf Boutaba, Aladdin Saleh	Active Learning for Transformer-Based Fault Diagnosis in 5G and Beyond Mobile Networks	2026	Vol. 23, Issue	Transformers Fault diagnosis Data models	As 5G and beyond mobile networks evolve, their increasing complexity necessitates advanced, automated, and data-driven 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
Deqiang Zhou, Xinsheng Ji, Wei You, Hang Qiu, Yu Zhao, Mingyan Xu	Intent-Based Automatic Security Enhancement Method Toward Service Function Chain	2026	Vol. 23, Issue	Security Translation Servers	The reliance on Network Function Virtualization (NFV) and Software-Defined Network (SDN) introduces a wide variety of security risks in Service Function Chain (SFC), necessitating the implementation of automated security measures to safeguard ongoing service delivery. To address the security risks faced by online SFCs and the shortcomings of traditional manual configuration, we introduce Intent-Based Networking (IBN) for the first time to propose an automatic security enhancement method through embedding Network Security Functions (NSFs). However, the diverse security requirements and performance requirements of SFCs pose significant challenges to the translation from intents to NSF embedding schemes, which manifest in two main aspects. In the logical orchestration stage, NSF composition consisting of NSF sets and their logical embedding locations will significantly impact the security effect. So security intent language model, a formalized method, is proposed to express the security intents. Additionally, NSF Embedding Model Generation Algorithm (EMGA) is designed to determine NSF composition by utilizing NSF capability label model and NSF collaboration model, where NSF composition can be further formulated as NSF embedding model. In the physical embedding stage, the differentiated service requirements among SFCs result in NSF embedded model obtained by EMGA being a multi-objective optimization problem with variable objectives. Therefore, Adaptive Security-aware Embedding Algorithm (ASEA) featuring adaptive link weight mapping mechanism is proposed to solve the optimal NSF embedding schemes. This enables the automatic translation of security intents into NSF embedding schemes, ensuring that both security requirements are met and service performance is guaranteed. We develop the system instance to verify the feasibility of intent translation solution, and massive evaluations demonstrate that ASEA algorithm has better performance compared with the existing works in the diverse requirement scenarios.	10.1109/TNSM.2025.3635228
Josef Koumar, Timotej Smoleň, Kamil Jeřábek, Tomáš Čejka	Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting	2026	Vol. 23, Issue	Forecasting Telecommunication traffic Deep learning	Accurate network traffic forecasting is crucial for Internet service providers to optimize resources, improve user experience, and detect anomalies. Until recently, the lack of large-scale, real-world datasets limited the fair evaluation of forecasting methods. The newly released CESNET-TimeSeries24 dataset addresses this gap by providing multivariate traffic data from thousands of devices over 40 weeks at multiple aggregation granularities and hierarchy levels. In this study, we leverage the CESNET-TimeSeries24 dataset to conduct a systematic evaluation of state-of-the-art deep learning models and provide practical insights. Moreover, our analysis reveals trade-offs between prediction accuracy and computational efficiency across different levels of granularity. Beyond model comparison, we establish a transparent and reproducible benchmarking framework, releasing source code and experiments to encourage standardized evaluation and accelerate progress in network traffic forecasting research.	10.1109/TNSM.2025.3636557
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	2026	Vol. 23, Issue	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
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	2026	Vol. 23, Issue	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
Anurag Dutta, Sangita Roy, Rajat Subhra Chakraborty	RISK-4-Auto: Residually Interconnected and Superimposed Kolmogorov-Arnold Networks for Automotive Network Traffic Classification	2026	Vol. 23, Issue	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 $\approx 1.0163$ % for all-class classification and $\approx 2.5535$ % 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
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	2026	Vol. 23, Issue	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