TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Adel Chehade, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino	Hardware-Aware Neural Architecture Search for Encrypted Traffic Classification on Resource-Constrained Devices	2026	Early Access	Accuracy Computational modeling Cryptography	This paper presents a hardware-efficient deep neural network (DNN), optimized through hardware-aware neural architecture search (HW-NAS); the DNN supports the classification of session-level encrypted traffic on resource-constrained Internet of Things (IoT) and edge devices. Thanks to HW-NAS, a 1D convolutional neural network (CNN) is tailored on the ISCX VPN-nonVPN dataset to meet strict memory and computational limits while achieving robust performance. The optimized model attains an accuracy of 96.60% with just 88.26K parameters, 10.08M floating-point operations (FLOPs), and a maximum tensor size of 20.12K. Compared to state-of-the-art (SOTA) models, it achieves reductions of up to 444-fold, 312-fold, and 15-fold in these metrics, respectively, significantly minimizing memory footprint and runtime requirements. The model also demonstrates versatility, achieving up to 99.86% across multiple VPN and traffic classification (TC) tasks; it further generalizes to external benchmarks with up to 99.98% accuracy on USTC-TFC and QUIC NetFlow. In addition, an in-depth approach to header-level preprocessing strategies confirms that the optimized model can provide notable performance across a wide range of configurations, even in scenarios with stricter privacy considerations. Likewise, a reduction in the length of sessions of up to 75% yields significant improvements in efficiency, while maintaining high accuracy with only a negligible drop of 1-2%. However, the importance of careful preprocessing and session length selection in the classification of raw traffic data is still present, as improper settings or aggressive reductions can bring about a 7% reduction in overall accuracy. The quantized architecture was deployed on STM32 microcontrollers and evaluated across input sizes; results confirm that the efficiency gains from shorter sessions translate to practical, low-latency embedded inference. These findings demonstrate the method’s practicality for encrypted traffic analysis in constrained IoT networks.	10.1109/TNSM.2026.3666676
Jiazhong Lu, Jimin Peng, Jian Shu, Jiali Yin, Xiaolei Liu	Adversarial Sample Based on Structured Fusion Noise for Botnet Detection in Industrial Control Systems	2026	Early Access	Botnet Industrial control Feature extraction	The industrial control system’s artificial intelligence-based botnet intrusion detection system has a high detection performance and efficiency in an environment without interference. However, these systems are not immune to evasion through adversarial samples. In this study, we introduce a feature extraction technique tailored for ICS botnet detection. This approach classifies traffic packets based on network traffic attributes and ICS-specific identification codes, encompassing the statuses of ICS devices, enhancing detection precision. Meanwhile, this strategy addresses challenges in ICS data collection and bolsters experimental efficacy. To build a comprehensive botnet intrusion dataset within an ICS, we concurrently utilized existing ICS devices to collect both standard ICS and botnet traffic. Additionally, we present an innovative adversarial sample generation method for botnet detection models, integrating both time-domain and frequency-domain noise. Testing under three real-world ICS attack scenarios revealed our technique can markedly degrade the classification performance of eight leading AI-based detection models, emphasizing its potential for evading AI-based ICS intrusion detectors.	10.1109/TNSM.2026.3665504
Woojin Jeon, Donghyun Yu, Ruei-Hau Hsu, Jemin Lee	Secure Data Sharing Framework with Fine-grained Access Control and Privacy Protection for IoT Data Marketplace	2026	Early Access	Internet of Things Encryption Access control	The proliferation of IoT devices has led to an exponential increase in data generation, creating new opportunities for data marketplaces. However, due to the security and privacy issues arising from the sensitive nature of IoT data, as well as the need for efficient management of vast amounts of IoT data, a robust solution is necessary. Therefore, this paper proposes a secure data sharing framework with fine-grained access control and privacy protection for the internet of things (IoT) data marketplace. For fine-grained access control of the data in the proposed protocol, we develop the hidden attributes and encryption outsourced key-policy attribute-based encryption (HAEO-KP-ABE) that outsources high-complex operations to peripheral devices with high capability to reduce the computation burden of IoT device. It achieves data privacy by hiding attributes in the ciphertext and by preventing entities that do not hold the data consumer’s secret key material (including SA/CS) from running the match test on stored ciphertexts before decryption. It also has an efficient match test algorithm which can verify that the hidden attributes of the ciphertext match the access policy of the data consumer’s private key without revealing those attributes. We demonstrate the proposed protocol satisfies the security features required for the data sharing process in an IoT data marketplace environment. Furthermore, we evaluate the execution time of the proposed protocol according to the number of attributes and show the practicality and efficiency of the proposed protocol compared to the related works.	10.1109/TNSM.2026.3670207
Xing Li, Ge Gao, Zhaoyu Chen, Xin Li, Qian Huang	MD-PCSN: Meta-motion Decoupling Point Cloud Sequence Network for Privacy-Preserving Human Action Recognition in AI machines	2026	Early Access	Point cloud compression Convolution Three-dimensional displays	In next-generation communication networks and Industry 5.0 based applications, ensuring robust security and reliability in human-computer interaction (HCI) constitutes a fundamental prerequisite for safety-critical AI machine systems. Point cloud sequence-based human action recognition demonstrates intrinsic advantages in privacy-preserving HCI, leveraging its non-intrusive sensing modality to mitigate data vulnerability while maintaining high-precision action interpretation in industrial environments. Existing spatio-temporal encoding methods for point cloud sequence-based action recognition suffer from two fundamental limitations: (1) rigid neighborhood constraints impair multi-scale feature extraction for heterogeneous body parts, and (2) independent spatial-temporal decomposition introduces motion representation distortion. We propose a Meta-motion Decoupling Point Cloud Sequence Network (MD-PCSN) that addresses these challenges through: (1) logarithmic spatio-temporal point convolution for hierarchical meta-motion construction at variable granularities, and (2) a novel Gated-KANsformer architecture with differential motion encoding to explicitly model both short-term displacements and long-term spatio-temporal dependencies. The proposed meta-motion decoupling mechanism significantly enhances robustness against sensor perturbations, making the framework particularly suitable for security-critical applications. Extensive experiments on three benchmark datasets demonstrate MD-PCSN’s superior performance. It outperforms classic PST-Transformer by 1.5% on MSR Action3D and 4.14% on UTD-MHAD. Under the NTU RGB+D 60, it achieves 2.9% cross-view gain over the latest PointActionCLIP.	10.1109/TNSM.2026.3671357
Guolong Li, Yuan Gao, Jiongjiong Ren, Shaozhen Chen	BPF-GNN: A multi-granularity feature extraction model using graph neural networks for encrypted traffic classification	2026	Early Access	Feature extraction Cryptography Payloads	Encrypted traffic classification is crucial for critical network management tasks such as traffic type identification, resource allocation, and risk mitigation, especially given that encrypted traffic has become the dominant form of modern network communication. However, existing classification methods are typically confined to single-level feature extraction, failing to capture the multi-granularity information inherent in traffic and thus limiting their ability to characterize complex encrypted traffic patterns. To address this issue, this paper proposes BPF-GNN, a hierarchical graph feature extraction model for encrypted traffic classification. The model enables multi-granularity feature learning by constructing a three-tier graph structure (Byte-, Packet-, and Flow-level). It sequentially extracts discriminative information inherent in each granularity level and accumulates multi-dimensional traffic characteristics, significantly improving the classification accuracy of encrypted traffic. Experiments on the ISCX-VPN2016, ISCX-Tor2016, USTC-TFC2016, and MIRAGE-2024 datasets demonstrate that BPF-GNN outperforms existing methods, validating the effectiveness and superiority of the proposed hierarchical multi-granularity feature extraction approach.	10.1109/TNSM.2026.3671203
Beibei Li	B-TWGA: A Trusted Gateway Architecture Based on Blockchain for Internet of Things	2026	Early Access	Internet of Things Blockchains Security	Internet of Things (IoT) terminals are commonly used for data sensing and edge control. The communication links between these hardware devices are critical points that are vulnerable to security attacks. Moreover, these links are usually composed of resource-constrained nodes that cannot implement strong security protections. To address these security threats, we introduce a Blockchain-based Trustworthy Gateway Architecture (B-TWGA), which does not rely on additional thirdparty management institutions or hardware facilities, nor does it require central control. Our proposal further considers the possibility of Denial of Service (DoS) attacks in blockchain transactions, ensuring secure storage and seamless interaction within the network. The proposed scheme offers advantages such as tamper-proofing, protection against malicious attacks, and reliability while maintaining operational simplicity. Experimental results demonstrate that B-TWGA maintains stable trust levels even when 40% of the network nodes are malicious, effectively mitigates trust degradation caused by vote-stuffing and switch attacks, and ensures high transaction processing performance, achieving an average throughput of 97.55% for storage transactions with practical response times below 0.7s for typical trust file sizes.	10.1109/TNSM.2026.3671208
Yuanzhen Jiang, Yaqiong Liu, Xidian Wang, Nan Cheng, Zihan Jia, Duo Shi, Zhe Lv, Zhouyuan Li, Yan Zhang	Entity-level Autoregressive Relational Triple Extraction toward Knowledge Graph Construction for Network Operation and Maintenance	2026	Early Access	Knowledge graphs Tagging Maintenance	With the significant increase of communication network scales, intelligent Network Operation and Maintenance (NOM) becomes essential. Knowledge Graphs (KGs) are a key enabler for intelligent NOM, and Relational Triple Extraction (RTE) plays a critical role in KG construction. However, most existing RTE researches rely on general-domain corpora, with limited exploration into the specialized domain. In this paper, we identify a novel challenge in Chinese NOM corpus — Segmented Entity, which has garnered little attention in prior works. To address it, this paper proposes an Entity-level Autoregressive RTE (EARTE) method, which incorporates an innovative Segmented-BIO (Begin, Inside, Outside) tagging scheme. Furthermore, we construct the CMIM23-NOM1-RA, the first high-quality restricted domain RTE dataset for NOM. Throughout the experimentation, we meticulously reproduce all baselines and provide a comprehensive analysis. The results show that EARTE achieves the best performance on CMIM23-NOM1-RA. EARTE’s F1 scores surpass those of the best-performing baselines by 0.4%, 2.7%, and 0.8% under the strict criterion, the lenient criterion, and the setting focusing only on segmented entities, respectively. Finally, our codes, dataset, and reproduction guidelines are publicly available at: https://github.com/JYzzzzzz/PEAR-RTE.	10.1109/TNSM.2026.3671463
Shi Dong, Fuxiang Zhao, Longhui Shu, Junjie Huang	Android Zero-Day Guard: Zero-Shot Malware Detection Using Deep Learning and Generative Models	2026	Early Access	Malware Feature extraction Accuracy	This paper proposes an Android-oriented zero-day malware detection method named ”Android Zero-Day Guard.” By integrating deep neural networks with zero-shot learning, this approach is capable of identifying emerging threats without prior exposure to malicious samples. The method converts APK files into images and extracts deep features, enabling effective capture of behavioral malware patterns. Experimental results demonstrate that the proposed method achieves a precision of 94.93%, a recall of 93.75%, and an F1-score of 94.28% across multiple malware families. Without relying on dynamic analysis, it exhibits strong detection capability and generalization performance, making it well-suited for the early identification of emerging threats. While the model performs strongly on benchmark datasets, continuous validation on the latest families is essential for deployment in a rapidly evolving threat landscape.	10.1109/TNSM.2026.3671305
Ebrima Jaw, Moritz Müller, Cristian Hesselman, Lambert Nieuwenhuis	Reproducibility Study and Assessment of the Evolution of Serial BGP Hijacking Events	2026	Early Access	Internet Routing Border Gateway Protocol	The Border Gateway Protocol (BGP) is the Internet’s most crucial protocol for efficient global connectivity and traffic routing. However, BGP is well known to be susceptible to route hijacks and leaks. Route hijacks are the intentional or unintentional illegitimate announcements of network resources that can compromise the confidentiality, integrity, and availability of communication systems. In the past, the so-called “serial hijackers” have hijacked Internet resources multiple times, some lasting for several months or years. So far, only the paper “Profiling BGP Serial Hijackers” has explicitly focused on these repeat offenders, and it dates back to 2019. Back then, they had to process large amounts of BGP announcements to find a few potential serial hijackers. In this paper, we revisit the profiling of serial hijackers. We reproduced the 2019 study and showed that we can identify potential offenders with less data while achieving similar accuracy. Our study confirms that there has been no significant increase in the evolution of serial hijacking activities in the last five years. We then extend their research, further analyze the characteristics of the serial hijackers, and show that most of the alleged serial hijackers are still active on the Internet. We also find that 22.9% of the hijacks violated RPKI objects but were still widely propagated, and that even MANRS participants were among the propagating networks.	10.1109/TNSM.2026.3671613
Shaohui Gong, Luohao Tang, Jianjiang Wang, Quan Chen, Cheng Zhu	A Key Node Set Analysis Method For Regional Service Denial In Mega-Constellation Networks	2026	Early Access		Mega-constellation networks (MCNs) face the significant threats of regional service denial attacks. To improve the robustness of regional services in MCNs against such attacks, a cost-effective approach is to identify key node sets for targeted protection efforts. This paper formally defines the key node set analysis problem for regional service denial in MCNs and develops a comprehensive solution framework. First, we develop a regional service capability analysis model that considers the dynamic collaboration of multiple satellites within regional communication service scenarios in MCNs, alongside a temporal network model for their collaborative relationships. Next, we design a multi-satellite criticality metric that quantifies the multi-dimensional impacts of satellite node set failures on regional service capabilities. Building on these, we construct a mixed-integer programming-based key node set analysis model to achieve precise identification of key node sets. Finally, simulation experiments are conducted to verify and analyze the proposed methods, providing insights to enhance the robustness of regional services in MCNs.	10.1109/TNSM.2026.3672157
Tuan-Vu Truong, Van-Dinh Nguyen, Quang-Trung Luu, Phi-Son Vo, Xuan-Phu Nguyen, Fatemeh Kavehmadavani, Symeon Chatzinotas	Accelerating Resource Allocation in Open RAN Slicing via Deep Reinforcement Learning	2026	Early Access	Resource management Open RAN Ultra reliable low latency communication	The transition to beyond-fifth-generation (B5G) wireless systems has revolutionized cellular networks, driving unprecedented demand for high-bandwidth, ultra low-latency, and massive connectivity services. The open radio access network (Open RAN) and network slicing provide B5G with greater flexibility and efficiency by enabling tailored virtual networks on shared infrastructure. However, managing resource allocation in these frameworks has become increasingly complex. This paper addresses the challenge of optimizing resource allocation across virtual network functions (VNFs) and network slices, aiming to maximize the total reward for admitted slices while minimizing associated costs. By adhering to the Open RAN architecture, we decompose the formulated problem into two subproblems solved at different timescales. Initially, the successive convex approximation (SCA) method is employed to achieve at least a locally optimal solution. To handle the high complexity of binary variables and adapt to time-varying network conditions, traffic patterns, and service demands, we propose a deep reinforcement learning (DRL) approach for real-time and autonomous optimization of resource allocation. Extensive simulations demonstrate that the DRL framework quickly adapts to evolving network environments, significantly improving slicing performance. The results highlight DRL’s potential to enhance resource allocation in future wireless networks, paving the way for smarter, self-optimizing systems capable of meeting the diverse requirements of modern communication services.	10.1109/TNSM.2026.3665553
Honghao Gao, Qionghuizi Ran, Ye Wang, Yueshen Xu	BiTrustChain: A Dual-Blockchain Empowered Dynamic Vehicle Trust Management for Malicious Detection in IoV	2026	Early Access	Blockchains Vehicle dynamics Trust management	The rapid development of the Internet of Vehicles (IoV) has accelerated technological progress, but several critical security challenges remain, especially in the context of vehicle trust management. Two representative issues are malicious nodes and unreliable information transmission. To address these problems, we propose BiTrustChain, a dual-layer blockchain framework designed to enhance security and trust management in IoV environments. First, it consists of two innovative data chains: a Behavior Data Chain (BDC) and a Reputation Evaluation Chain (REC). The BDC records vehicle interaction data, whereas the REC stores and updates the trust values in real time. Second, within this framework, we develop a Multifactor Bayesian Reputation (MFBR) model that enables quantitative evaluation of node trustworthiness. It integrates a time-decay function and a penalty mechanism to regulate reputation evolution. The trust values decrease after malicious behaviors and recover through continuous normal interactions. In addition, we propose a dynamic local whitelist for indirect reputation evaluation. It filters out untrustworthy nodes and ensures that only reliable nodes remain. The filtered indirect trust is then combined with direct trust to produce a comprehensive reputation score. Third, we design a new set of event-driven smart contracts to synchronize the BDC and REC in real time and ensure secure and efficient data exchange. Finally, we performed experiments on the evaluation platform SUMO/NS-3, and the results show that our method identifies malicious nodes with higher accuracy. In particular, the framework achieves 1.5× higher throughput and reduces latency by 40% compared to the baseline single-chain system. The framework also enhances interaction data integrity and improves robustness against adversarial reputation manipulation.	10.1109/TNSM.2026.3670385
Wenxuan Li, Yu Yao, Ni Zhang, Chuan Sheng, Ziyong Ran, Wei Yang	IMADP: Imputation-based Anomaly Detection in SCADA Systems via Adversarial Diffusion Process	2026	Early Access	Anomaly detection Adaptation models Data models	As the confrontation of the industrial cybersecurity upgrades, multi-dimensional variables measured by the SCADA multi-sensor are critical for assessing security risks in industrial field devices. While Deep Learning (DL) methods based on generative models have demonstrated effectiveness, the impact of missing features in samples and temporal window size on modeling and detection processes has been consistently overlooked. To address these challenges, this work proposes an IMADP framework that integratively solves two tasks of missingness patching and anomaly detection. Firstly, the Window-based Adaptive Selection Strategy (WASS) is also designed to intelligently window samples, reducing reliance on prior settings. Secondly, an imputer is constructed under WASS to restore sample integrity, which is implemented by a fully-connected network centered on Neural Controlled Differential Equations (NCDEs). Thirdly, a adversarial diffusion detection model with the variant Transformer as the inverse solver is proposed. Additionally, the Adaptive Dynamic Mask Mechanism (ADMM) is built upon to bolster the model’s comprehension of inter-dependencies between time and sensor nodes. Simultaneously, adversarial training is introduced to optimize training and detection latency caused by the excessive diffusion step size during the native Conditional Diffusion process. The experimental results validate that the proposed framework has the capability to build detectors using missing training samples, and its overall detection performance, tested across six datasets, is superior to existing methods.	10.1109/TNSM.2026.3670062
Vaishnavi Kasuluru, Luis Blanco, Cristian J. Vaca-Rubio, Engin Zeydan, Albert Bel	AI-Empowered Multivariate Probabilistic Forecasting: A Key Enabler for Sustainability in Open RAN	2026	Early Access	Open RAN Forecasting Probabilistic logic	This paper explores the role of multivariate probabilistic forecasting in improving O-RAN operations, focusing on network sustainability aspects. A comprehensive analysis of its potential benefits and challenges, as well as its integration into the O-RAN architecture are described. The paper first presents an overview of the O-RAN architecture and components, followed by an examination of power consumption models relevant to O-RAN deployments and the challenges associated with traditional deterministic models in resource allocation. We then examine the performance of several state-of-the-art probabilistic multivariate forecasting techniques namely, Gaussian Process Vector Autoregression (GPVAR), Temporal Fusion Transformer (TFT) and non-probabilistic multivariate technique namely, Multivariate Long-Short Term Memory (LSTM) and explain their implementation details and provide their evaluations. The simulation results show the effectiveness of these techniques in predicting Physical Resource Block (PRB) utilization and optimizing resource allocation. In particular, significant energy savings – around 20-30%– are achieved, depending on the percentile of the used probabilistic forecasting techniques. The benefits of considering probabilistic forecasting techniques compared to multi-variate LSTM are also analyzed. Our results emphasize the potential of probabilistic forecasting to improve energy efficiency and sustainability in O-RAN operations.	10.1109/TNSM.2026.3669847
Zhaoping Li, Mingshu He, Xiaojuan Wang	HKD-Net: Hierarchical Knowledge Distillation Based on Multi-Domain Feature Fusion for Efficient Network Intrusion Detection	2026	Early Access	Feature extraction Telecommunication traffic Knowledge engineering	We propose HKD-Net1, a hierarchical knowledge distillation network based on multi-domain feature fusion, for efficient network intrusion detection on resource-constrained edge devices. The framework incorporates dedicated feature extraction modules across temporal, frequency, and spatial domains, and introduces a dynamic gating mechanism for adaptive feature fusion, resulting in a more discriminative and comprehensive feature representation. Moreover, a hierarchical distillation mechanism is designed that not only preserves soft labels from the output layer but also aligns intermediate features from spatial, temporal, frequency, and fused domains, enabling efficient knowledge transfer from a large teacher model to a compact student model. Through knowledge distillation, the final lightweight model requires only 278,580 parameters, reducing the number of parameters by approximately 74.68% compared to the teacher, while maintaining high detection accuracy. Extensive experiments on three public datasets (Kitsune, CIRA-CIC-DoHBrw2020, and CICIoT2023) demonstrate that HKD-Net outperforms five state-of-the-art methods, achieving accuracies of 96.72%, 97.19%, and 87.19%, respectively, while reducing parameters by 74.68% and maintaining low computational cost.	10.1109/TNSM.2026.3668812
Deemah H. Tashman, Soumaya Cherkaoui	Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks	2026	Early Access	Reconfigurable intelligent surfaces Reliability Optimization	Cognitive radio networks (CRNs) are a key mechanism for alleviating spectrum scarcity by enabling secondary users (SUs) to opportunistically access licensed frequency bands without harmful interference to primary users (PUs). To address unreliable direct SU links and energy constraints common in next-generation wireless networks, this work introduces an adaptive, energy-aware hybrid reconfigurable intelligent surface (RIS) for underlay multiple-input single-output (MISO) CRNs. Distinct from prior approaches relying on static RIS architectures, our proposed RIS dynamically alternates between passive and active operation modes in real time according to harvested energy availability. We also model our scenario under practical hardware impairments and cascaded fading channels. We formulate and solve a joint transmit beamforming and RIS phase optimization problem via the soft actor-critic (SAC) deep reinforcement learning (DRL) method, leveraging its robustness in continuous and highly dynamic environments. Notably, we conduct the first systematic study of reward poisoning attacks on DRL agents in RIS-enhanced CRNs, and propose a lightweight, real-time defense based on reward clipping and statistical anomaly filtering. Numerical results demonstrate that the SAC-based approach consistently outperforms established DRL base-lines, and that the dynamic hybrid RIS strikes a superior trade-off between throughput and energy consumption compared to fully passive and fully active alternatives. We further show the effectiveness of our defense in maintaining SU performance even under adversarial conditions. Our results advance the practical and secure deployment of RIS-assisted CRNs, and highlight crucial design insights for energy-constrained wireless systems.	10.1109/TNSM.2026.3660728
Pengcheng Guo, Zhi Lin, Haotong Cao, Yifu Sun, Kuljeet Kaur, Sherif Moussa	GAN-Empowered Parasitic Covert Communication: Data Privacy in Next-Generation Networks	2026	Early Access	Interference Generators Generative adversarial networks	The widespread integration of artificial intelligence (AI) in next-generation communication networks poses a serious threat to data privacy while achieving advanced signal processing. Eavesdroppers can use AI-based analysis to detect and reconstruct transmitted signals, leading to serious leakage of confidential information. In order to protect data privacy at the physical layer, we redefine covert communication as an active data protection mechanism. We propose a new parasitic covert communication framework in which communication signals are embedded into dynamically generated interference by generative adversarial networks (GANs). This method is implemented by our CDGUBSS (complex double generator unsupervised blind source separation) system. The system is explicitly designed to prevent unauthorized AI-based strategies from analyzing and compromising signals. For the intended recipient, the pretrained generator acts as a trusted key and can perfectly recover the original data. Extensive experiments have shown that our framework achieves powerful covert communication, and more importantly, it provides strong defense against data reconstruction attacks, ensuring excellent data privacy in next-generation wireless systems.	10.1109/TNSM.2026.3666669
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
Mahnoor Sajid, Mohib Ullah Khan, KyungHi Chang	Intelligent Xn-Based Energy Aware Handover Optimization in 5G Networks via NWDAF-Orchestrated Agent Framework	2026	Early Access	Handover 5G mobile communication Quality of service	Energy-aware mobility management in dense Fifth Generation (5G) networks is increasingly challenged by frequent handovers and unnecessary activation of sleeping next-generation NodeBs (gNBs), which lead to excessive energy consumption and degraded mobility reliability. Conventional Xn-based handover schemes rely on static radio thresholds and implicitly assume always-active gNBs, while existing NWDAF-enabled approaches improve stability but do not explicitly account for energy costs during handover execution. To address these limitations, this paper proposes the Intelligent Energy-Aware Handover Framework (IEAHF), a NWDAF-orchestrated architecture that integrates coordinated agent-based control for energy-aware mobility management. The proposed framework introduces an Energy-Aware Handover Optimization Agent (EA-HOA) to guide reliability-driven handover decisions and a Handover Energy Evaluation Agent (HEEA) to assess the energy impact of candidate handover actions, with both agents operating within a closed-loop control process enforced through Operations, Administration, and Maintenance (OAM). By reformulating handover success to incorporate energy inefficiency and enabling per-handover gNB energy reasoning at decision time, IEAHF jointly optimizes service continuity and energy efficiency. System-level simulations demonstrate that the proposed Agent-NWDAF configuration consistently outperforms baseline and analytics-only schemes, achieving tightly concentrated Energy-Aware Handover Success Rates of approximately 0.98, reducing the Energy-Aware Handover Failure Rate to the range of 0.007–0.015, and delivering up to a 32% reduction in average gNB power consumption relative to an always-on baseline. These results indicate that IEAHF provides a scalable and effective solution for energy-efficient mobility management in 5G networks and establishes a foundation for energy-aware handover control in future Sixth Generation (6G) systems.	10.1109/TNSM.2026.3668238
Fernando Martinez-Lopez, Lesther Santana, Mohamed Rahouti, Abdellah Chehri, Shawqi Al-Maliki, Gwanggil Jeon	Learning in Multiple Spaces: Prototypical Few-Shot Learning with Metric Fusion for Next-Generation Network Security	2026	Early Access	Measurement Prototypes Extraterrestrial measurements	As next-generation communication networks increasingly rely on AI-driven automation, ensuring robust and secure intrusion detection becomes critical, especially under limited labeled data. In this context, we introduce Multi-Space Prototypical Learning (MSPL), a few-shot intrusion detection framework that improves prototype-based classification by fusing complementary metric-induced spaces (Euclidean, Cosine, Chebyshev, and Wasserstein) via a constrained weighting mechanism. MSPL further enhances stability through Polyak-averaged prototype generation and balanced episodic training to mitigate class imbalance across diverse attack categories. In a few-shot setting with as few as 200 training samples, MSPL consistently outperforms single-metric baselines across three benchmarks: on CICEVSE Network2024, AUPRC improves from 0.3719 to 0.7324 and F1 increases from 0.4194 to 0.8502; on CICIDS2017, AUPRC improves from 0.4319 to 0.4799; and on CICIoV2024, AUPRC improves from 0.5881 to 0.6144. These results demonstrate that multi-space metric fusion yields more discriminative and robust representations for detecting rare and emerging attacks in intelligent network environments.	10.1109/TNSM.2026.3665647