Transactions on Emerging Telecommunications Technologies最新文献

{"title":"AMTA: An Innovative Privacy-Aware Adaptive Transformer for Real-Time Multimodal Data Fusion","authors":"Murad A. A. Almekhlafi,&nbsp;Fadwa Alrowais,&nbsp;Saied Alshahrani,&nbsp;Mohammed Maray,&nbsp;Mohammed A. AlAqil,&nbsp;Mohannad A. Alharbi,&nbsp;Abdulsamad Ebrahim Yahya,&nbsp;Radwa Marzouk","doi":"10.1002/ett.70365","DOIUrl":"https://doi.org/10.1002/ett.70365","url":null,"abstract":"<div>\u0000 \u0000 <p>In this research, we introduce AMTA, an innovative adaptive multimodal transformer algorithm that transforms concurrent data integration in real-time. This flexibility aids AMTA in optimizing contextual understanding and assuming the forefront of multimodal representation learning using adaptive attention mechanisms that dynamically balance the attention assigned to various modalities like text, images, or sensor feeds according to contextual need, unlike fixed modalities in older frameworks. AMTA is widely accurate (92.4%) and fast (120 ms latency), making it suitable for the time-sensitive world such as healthcare diagnostics, autonomous vehicle navigation, and so forth. At AMTA, we find a novel way to address privacy and performance, combining federated learning with lightweight encryption for confidential data processing. This makes AMTA ideal for healthcare, defense, and autonomous systems, where it is crucial to handle sensitive data securely. Explainability is a big feature, as SHAP and LIME techniques provide insight into how AMTA makes decisions. Novel adaptive attention visualizations further improve interpretability in the model, allowing the user to better interpret which data modality, such as medical images or patient records, is contributing the most at predicting the outcome. AMTA musters a higher accuracy (92.4% vs. 89.2% vs. 90.1%) and lower latency (120 vs. 150 vs. 140 ms) than both Flamingo and Perceive IO, making it an effective multimodal solution. In future works, further optimizations of AMTA aimed at its adoption in edge devices, among other usages across people-centric domains such as smart cities, robotics, and intelligent agriculture, will set a gold standard for the development of ethical, scalable AI.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain Integrated Hierarchical Double Deep Q-Learning for Optimized Aerial Base Station Deployment in 6G Networks for Smart Cities","authors":"J. Sasidevi,&nbsp;Aditya Bommaraju,&nbsp;B. Srinivasa Rao,&nbsp;G. Manikandan","doi":"10.1002/ett.70380","DOIUrl":"10.1002/ett.70380","url":null,"abstract":"<div>\u0000 \u0000 <p>The advent of 6G networks introduces new challenges in the deployment and management of Aerial Base Stations (ABSs) requiring advanced techniques to optimize network performance across multiple conflicting objectives such as coverage, energy efficiency, interference management, and security. This paper presents a novel Blockchain integrated Hierarchical Double Deep Q-Learning (BH-DDQL) framework designed to address these challenges by leveraging reinforcement learning for dynamic ABS positioning and operational management. Its hierarchical structure enables a multi-layered decision-making process breaking down the ABS deployment problem into manageable sub-problems, each optimized through reinforcement learning. At the top level, BH-DDQL handles strategic decisions such as ABS positioning to maximize coverage and minimize interference. At the lower level, it fine-tunes parameters like power control and resource allocation to enhance energy efficiency and service quality. The BH-DDQL framework solves the challenge of optimal positioning by utilizing a dynamic swarm-enhanced mutated slime mold optimization (DSEMSMO) algorithm, which finds effective initial placements for ABSs. To manage energy constraints, the framework incorporates energy-aware decision-making within the reinforcement learning process, ensuring that ABSs operate efficiently over extended periods. Interference is minimized through a multi-objective reward function that balances coverage and interference, ensuring that the ABSs provide optimal service without causing excessive interference to other network elements. Security concerns are addressed by integrating blockchain technology, which secures ABS operations and data through a tamper-proof log and transparent decision-making process. The BH-DDQL framework employs Pareto optimality for deployment configuration, ensuring that no single performance metric is improved at the expense of others. The iterative learning process allows the ABSs to adjust to changing network conditions and environmental factors, securing optimal performance over time. The simulation is conducted in NS-3 and the numerical results show that the proposed framework achieves a coverage probability of 96.8%, an energy consumption of 500 joules, and a throughput of 10.5 gigabits per second, representing significant advancements over existing systems.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Encrypted Dynamic RVFLN Ensemble Learning for Privacy-Preserving State-of-Health Prediction in Electric Vehicle Batteries Using Fully Homomorphic Encryption CKKS","authors":"Vankamamidi Srinivasa Naresh,&nbsp;Vanapalli Sai Sriram,&nbsp;Ayyappa Dullam","doi":"10.1002/ett.70388","DOIUrl":"10.1002/ett.70388","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a novel privacy-preserving approach for accurately predicting the State-of-Health (SoH) of electric vehicle (EV) batteries by integrating Dynamic Random Vector Functional Link Network (RVFLN) models with Ensemble Learning under the framework of Fully Homomorphic Encryption (FHE). To ensure data confidentiality throughout the predictive process, the scheme employs the CKKS (Cheon–Kim–Kim–Song) approximate homomorphic encryption, which enables efficient computations on encrypted floating-point data commonly encountered in SoH estimation. The proposed method, called Encrypted Dynamic RVFLN Ensemble Learning (EDRVFLN), leverages the adaptability of dynamic RVFLNs to capture complex nonlinear battery behavior. Ensemble Learning is employed to enhance robustness and accuracy, whereas CKKS encryption guarantees that all learning and inference occur on encrypted data, preserving privacy without compromising model performance. Experimental validation using the publicly available NASA battery dataset demonstrated that EDRVFLN achieved superior prediction accuracy compared with traditional unencrypted models. Importantly, the privacy-preserving capability of this method ensures that the sensitive operational and usage data remain secure throughout the analysis pipeline. This study represents a significant advancement in privacy-preserving predictive analytics for electric mobility. By demonstrating the feasibility and effectiveness of processing encrypted data using CKKS in a complex ensemble learning setup, this study opens new pathways for secure machine learning applications in EV systems and other domains that require strong privacy guarantees alongside analytical precision.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient SLAM Algorithm Based on Quaternions for Real-Time Navigation of Unmanned Aerial Vehicles","authors":"Xin Wang,&nbsp;Manyu Wang,&nbsp;Xin Liu","doi":"10.1002/ett.70386","DOIUrl":"10.1002/ett.70386","url":null,"abstract":"<div>\u0000 \u0000 <p>Efficient quaternion-based Simultaneous Localization and Mapping framework designed for real-time navigation of Unmanned Aerial Vehicles in GPS-denied environments is presented here. Unlike conventional Euler angle or matrix-based SLAM approaches, the proposed system employs quaternion-based propagation in Inertial Measurement Unit (IMU) module, thereby eliminating singularities such as gimbal lock and ensuring numerically stable orientation estimation. It integrates Quaternion Error-State Extended Kalman Filter for incremental fusion of visual and inertial measurements and incorporates an incremental pose-graph backend with quaternion retraction to reduce drift during long-term navigation. Novelty lies in unifying quaternion mathematics with lightweight incremental optimization, thereby achieving a balance between accuracy and computational feasibility for real-time onboard deployment. Experimental validation on KAIST Visual-Inertial Odometry dataset demonstrates significant performance gains. Raw IMU is −50 Hz (20–25 ms/frame), far exceeding latency constraints of UAV flight dynamics. Additional analysis confirmed robustness with an average UAV speed of 1261.98 m/s, velocity stability (std. dev. 689.43 m/s) and reliable feature tracking (&gt; 1800 map points reconstructed). These findings establish the framework as a novel, lightweight and robust SLAM solution suitable not only for UAVs but also for space–air–ground autonomous systems requiring accurate real-time navigation under computational constraints.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Caviar Parrot Optimizer: A Quantum Key Distribution Approach to Secure Authentication and Data Storage","authors":"Dangety Sowjanya,&nbsp;Vidyadhari Chalasani,&nbsp;Ramachandro Majji,&nbsp;Cristin Rajan","doi":"10.1002/ett.70382","DOIUrl":"10.1002/ett.70382","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud computing enables flexible data access and storage, but introduces serious security concerns due to its shared and open nature. Secure authentication is essential to prevent unauthorized access, data breaches, and identity theft. Traditional methods are increasingly ineffective, especially against evolving threats like quantum attacks. Hence, there is a growing need for robust, scalable, and quantum-resilient authentication solutions to ensure data privacy and trust in cloud environments. Hence, this paper proposes a Caviar Parrot Optimizer (CaV-PO) to generate the optimal secret key for secure authentication and data storage. The data is accessed by numerous entities, like the Client System, Authentication Server, Primary Healthcare Center (PHC), Aadhaar Server (ADS), and Citizen Health Information Management System (CHIMS). A multi-phase secure authentication and data storage protocol, like Initialization and Registration, Authentication, Quantum Key agreement, Data encryption, and Data storage, is included in this research to ensure data privacy and secure communication between systems. Initially, the basic information of the user is registered, and then authentication is established to validate the user's identity. The optimal secret key is generated in the Quantum Key agreement phase, between entities using the developed CaV-PO scheme to secure communication. Then, sensitive data is encrypted to ensure its confidentiality and prevent unauthorized access, and finally, data is stored securely in distributed storage systems. Experimental results indicate that CaV-PO achieves a computation cost of 51.867 s, normalized variance of 0.860, and conditional privacy of 0.866, demonstrating its effectiveness in enhancing cloud security and privacy.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Behavior-Aware Machine Learning Framework for Geolocation Integrity Verification in VANET Environments","authors":"Mofadal Alymani,&nbsp;Fadwa Alrowais,&nbsp;Mohammed H. Alghamdi,&nbsp;Mohammed Alqahtani,&nbsp;Wahida Mansouri,&nbsp;Hussain Alshahrani,&nbsp;Ali Abdulaziz Alzubaidi,&nbsp;Radwa Marzouk","doi":"10.1002/ett.70384","DOIUrl":"10.1002/ett.70384","url":null,"abstract":"<div>\u0000 \u0000 <p>Vehicular Ad Hoc Networks (VANETs) depend primarily on accurate location data to enable connected driving and traffic safety. However, these systems still struggle with spoofing, falsification, and malicious behavior-based impacts. In a highly dynamic vehicular environment, even minor disturbances in reported positions can crash the entire network and lead to incorrect safety decisions. Existing intrusion detection systems (IDSs) commonly rely on single-sensor thresholds and computationally intensive models. Such threshold-based models fail to detect hidden attacks and are not suitable for resource-constrained environments. This study is the first to suggest the usage of a load-free model to meet future VANET demand. Motivated by the need for continuous trajectory monitoring, low-latency decision-making, and behavior-aware detection, this work introduces a lightweight, effective geolocation integrity framework for real-time VANET operations. A feather-inspired (quill) model that combines the strength of advanced lightweight modules. Recent studies highlight the requirement for behavior-aware security techniques to detect signal anomalies in vehicle movement patterns. However, most existing solutions either lack temporal behavioral modeling or suffer from high computational load. To avoid these challenges, we suggest using our QuillNet model in real-time VANET environments. The novelty of the QuillNet depends on its unified integration of noise reduction, temporal behavioral learning, and lightweight classification within a single pipeline. The main aim of QuillNet is to detect multiple spoofing and false-location signals by maintaining low computational cost for resource-constrained vehicular platforms. QuillNet integrates the strengths of a tiny denoising autoencoder (T-DAE), a long-term-short-term autoencoder (LSTM-AE), and a lightweight, float-efficient Light Gradient Boosting Machine (LightGBM) classifier for real-time attack detection. This hybrid design is effective against both sudden falsification and slow drift attacks. The model is evaluated on the publicly available “VANET Malicious Nodes dataset,” combined with synthetic geolocation-spoofing attacks. Through comprehensive experiments and comparative analysis, the proposed model demonstrates its efficiency, achieving 98.8% overall detection accuracy with an average detection latency of 0.18–1.5 s.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneous Searchable Signcryption With Cryptographic Reverse Firewalls for a UAV Network","authors":"Mazin Taha,&nbsp;Rashad Elhabob,&nbsp;Ting Zhong,&nbsp;Hu Xiong,&nbsp;Saru Kumari,&nbsp;Nabeil Eltayieb","doi":"10.1002/ett.70379","DOIUrl":"10.1002/ett.70379","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid expansion of the Internet of Things (IoT) has accelerated the widespread adoption of unmanned aerial vehicles (UAVs) in various applications, including traffic monitoring. Nevertheless, storing UAV-collected data on the cloud introduces significant concerns regarding data confidentiality and security. Preserving the privacy of UAV data through encryption is feasible, yet this approach compromises the flexibility and ease of searching and retrieving the encrypted data. This paper introduces a novel heterogeneous searchable signcryption with cryptographic reverse firewalls (HSS-CRF) scheme to enhance the usability of searchable encrypted UAV data and ensure confidentiality. The HSS-CRF protocol establishes a secure communication channel from the sender's UAV site in a certificateless cryptography (CLC) environment to the authorized user utilizing a public key infrastructure (PKI) cryptosystem enhanced by CRF. Furthermore, the HSS-CRF scheme robustly counters threats from internal keyword guessing attacks (IKGAs), algorithm substitution attacks (ASAs), and keyword guessing attacks (KGA). Our analysis demonstrates that the scheme achieves a reduction in computational and communication overhead, making it suitable for resource-constrained UAV environments.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DLB-IoT IDS: Deep Learning and Blockchain Based IoT Intrusion Detection and Prevention System","authors":"Vaishali Soni,&nbsp;Deepika Kukreja","doi":"10.1002/ett.70378","DOIUrl":"10.1002/ett.70378","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things (IoT) ecosystem is growing rapidly, which has made ensuring the security and integrity of connected devices and transmitted data a significant challenge. In contrast, numerous research efforts have integrated deep learning with blockchain to address intrusion detection related issues. But still, there are some issues in terms of prevention capability, accuracy, and system robustness. In order to detect and prevent cyberattacks in IoT environments, this paper offers a new intrusion detection and prevention system (DLB-IoT-IDS) that is based on blockchain technology. The system introduces a multilayered security architecture that leverages Elliptic Curve Cryptography (ECC) for lightweight key generation, smart contracts for secure key registration, and a proof of authority (PoA) mechanism for validating legitimate nodes and transactions on the blockchain. A novel Elite Chaotic based Golden Jackal Optimization (Ec-GJO) algorithm is employed for optimal feature selection, improving detection precision. For classification, a uniquely designed Probabilistic Dwarf mongoose self-attention based dense assisted Bi-LSTM (PDSAttdBi-LSTM) is introduced, which enhances detection accuracy and reduces false positive rates. The proposed model can obtain an accuracy of 99.72% and 99.80% for the UNSW-NB 15 dataset and the ToN-IoT dataset. The proposed model is better for intrusion detection than other compared models. This approach offers a comprehensive and scalable solution for securing IoT networks through both prevention and detection mechanisms.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Black-Box Adversarial Attack With Nesterov-AdaX Momentum on Airport Ground Vehicle Surveillance","authors":"Zhengyang Zhao,&nbsp;Buhong Wang,&nbsp;Jiwei Tian,&nbsp;Weiwei Jiang,&nbsp;Yubing Wang","doi":"10.1002/ett.70383","DOIUrl":"10.1002/ett.70383","url":null,"abstract":"<div>\u0000 \u0000 <p>With the increasing global air traffic, the Airport Ground Vehicle Surveillance (AGVS) based on object detection has become essential for runway intrusion detection, gate position allocation, and emergency response implementation. However, the adversarial attack poses serious intelligent security threats to the AGVS system due to the hardware vulnerabilities of video wireless transmissions. To analyze the potential security, this study proposes a Black-box Adversarial Attack with Nesterov-AdaX Momentum (BAM-Attack). First, this method utilizes Nesterov-AdaX Momentum Iterative Module (NAIM) to incorporate Nesterov momentum and employs AdaX to adjust the independent learning rate of each weight for improving attack transferability. Second, the Dynamic Quasi-Hyperbolic Momentum Iteration Module (DQIM) is applied to stabilize the gradient descent direction, while the embedded dynamic step size prevents the local optima. Finally, the generated surrogate adversarial examples (AEs) are input into the Ensemble Attack with Weight Optimization (EAWO) to generate black-box AEs. The EAWO balances the contributions of different surrogates with limited queries. For the targeted attack, the BAM-Attack can reduce the detection accuracy of targeted objects by about 75%, while retaining the accuracy of non-targeted objects by about 60%. The experiments show that the BAM-Attack can effectively conduct targeted mislabeling and fabrication attacks on one-stage and two-stage object detections. This study appears to be the first black-box adversarial attack for AGVS systems, revealing a critical security vulnerability in Intelligent Air Traffic Management (IATM).</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum Federated Learning for DoS Attack Detection and Privacy Preserving of VANET: A Novel Hybrid Machine Learning Approach","authors":"Abdullah Baihan,&nbsp;Zabeeh Ullah,&nbsp;Azeem Irshad,&nbsp;Muhammad Shafiq,&nbsp;Jin-Ghoo Choi,&nbsp;Mohammed Amoon","doi":"10.1002/ett.70375","DOIUrl":"https://doi.org/10.1002/ett.70375","url":null,"abstract":"<div>\u0000 \u0000 <p>The integration of Vehicular Ad Hoc Networks (VANETs) has changed intelligent transportation systems by making it possible for vehicles, roadside units (RSUs), and traffic management infrastructure to talk to each other in real time. This feature makes the roads safer, more convenient for drivers, and more efficient for traffic, but it also makes VANET ecosystems vulnerable to many types of cyberattacks, including Denial-of-Service (DoS) and false data injection, which can be very dangerous for safety and privacy. Conventional security solutions frequently struggle to address the highly dynamic, decentralized, and latency-sensitive characteristics of VANET environments. Intrusion Detection Systems (IDS) powered by Artificial Intelligence (AI) have become promising solutions, but there are still issues with computational overhead, secure model updates, and data privacy in distributed vehicular networks. To address these challenges, we introduce Quantum Lightweight Federated Learning (FL), an innovative hybrid machine learning framework that integrates the exponential computational power of Quantum Computing (QC) with the decentralized, privacy-preserving advantages of FL. The suggested method combines knowledge distillation with the FL process to create a lightweight detection model that works well on vehicle nodes with limited resources. Moreover, QKD Encryption is used to protect model parameters during federated aggregation, making sure that end-to-end privacy is maintained without slowing down processing. Lastly, SHAP, an Explainable AI method, is used to make sense of the choices made by the proposed model. Using the CICDDoS-2019 dataset for experimental validation shows that the proposed model is strong, with an accuracy of 99.36%, a high recall rate of 99.53%, and a precision rate of 99.38% across different attack scenarios.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146256505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}