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Secure Handover in 5G-V2X: Detecting and Localizing DoS Attack to Ensure Reliable Communication

IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 7610-7623页

作者： Dey, Meenu Rani Patra, Moumita Indian Institute of Technology Guwahati Department of Computer Science and Engineering Assam Guwahati 781039 India

A fifth-generation vehicle-to-everything (5G-V2X) communication network has been created by combining the capabilities of vehicular networks and 5G technologies. This advanced network provides assurance of increased comfort, reliability, and security for vehicle user data. However, because of the high mobility, one of the major difficulties in 5G-V2X is the Handover (HO) process when a vehicle switches from one base station to another. This procedure is vulnerable to a variety of attacks, including impersonation, DoS, and Man in the Middle attacks. This study focuses specifically on the impersonation attack during the HO procedure, which can lead to critical DoS situations with potential real-life implications. The attack scenario involves rogue Femto Access Points (FAPs) exploiting the HO event. In this scenario, a malicious vehicle poses as a legitimate FAP, disrupting communication for nearby vehicles. To mitigate this attack, a novel attack detection model is proposed, relying on metrics like HO Failure Ratio (HFR) and Radio Link Failure (RLF). The adverse effects of this attack are demonstrated within the 5G-V2X context through measurable parameters such as packet loss rate, throughput, block rate, and HFR. Regarding the localization aspect, an ML-based multiclass classification mechanism is adopted. This mechanism leverages features particularly relevant to the handover scenario. A variety of classification methods, including k-Nearest Neighbor (kNN), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and Naive Bayes (NB), are employed for localization purposes. To enhance localization accuracy, an Ensemble Learning approach is implemented by involving the integration of KNN, LR, and RF techniques. Overall, the proposed approach effectively detects and localizes attackers during HO procedures, and also outperforms existing approaches. © 2000-2011 IEEE.

关键词： 5G-V2X DoS attack handover impersonation attack intrusion detection intrusion localization machine learning

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AI-Enhanced Multi-Dimensional Stochastic Process Modeling for QoS Analysis in 6G Networks Under Joint Fading and Dynamic Interference Constraints in $\alpha-\eta-\mu$ Environments

AI-Enhanced Multi-Dimensional Stochastic Process Modeling fo...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Deepak Upadhyay Alankrita Aggarwal Pardeep Singh Deepak Suresh Asudani Nookala Venu Aakash Singh Ranswal Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering University Institute of Engineering Apex Institute of Technology UIE Chandigarh University Mohali India School of Computer Science and Engineering Galgotias University Noida India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

In this paper, we investigate AI-enhanced multidimensional stochastic process modelling for QoS analysis in 6G networks subjected to $\alpha-\eta-\mu$ fading and dynamic interference constraints. Simulation results and heatmaps are utilized for the investigation of fading parameters such as shape factors and interference power, that impacts network performance. Our experimental findings show that AI-driven models provide markedly superior results in improving a number of QoS metrics, including throughput, latency, and packet loss compared to traditional methods. The research illustrates how AI improves connectivity, and operational stability of a network which adapts in times of fluctuating conditions. Our results demonstrate the ability of AI to preserve QoS while balancing performance and power, thereby providing a perspective for 6G network design and optimization.

关键词： Fading channels 6G mobile communication Adaptation models Analytical models Stochastic processes Packet loss Quality of service Throughput Stability analysis Optimization

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Analyzing the Effect of Eco-Friendly Branding on Consumer Purchasing Decisions on Sustainability in Marketing using Data Analytics 3

Analyzing the Effect of Eco-Friendly Branding on Consumer Pu...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Mageswaran, J. Tamilselvan, P. Tamilarasu, J. Sivasubramani, K. Devi, Tharani Sah, Raja Ram School of Management Studies Bannari Amman Institute of Technology Tamil Nadu India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamil Nadu India Nandha Engineering College Department of Management Studies Tamil Nadu India M.Kumarasamy College of Engineering Department of Management Studies Tamil Nadu India Gobi Arts and Science College Department of Management Studies Tamil Nadu India Government Engineering College Department of Computer Science and Engineering Bihar Jehanabad India

ISBN: (纸本)9798331542375

Background: Consumer buying behaviour is increasingly influenced by eco-friendly branding, thus sustainable practices are starting to take front stage in contemporary marketing plans. Companies under increased demand to link their brands with sustainable ideas as consumers become more conscious of the effects they have on the environment. Contribution: This study investigates how environmentally friendly branding influences purchasing decisions keeping customer attitudes as well as behaviour changes in mind. Findings: A comprehensive survey was given to 1,500 persons total throughout the duration of the research and statistical analysis was done with assistance from a simulation tool in order to evaluate changes in consumer sentiment and purchase behaviour. Results: Based on the findings, 48 percent of the participants were ready to pay extra for environmentally friendly products and 72 percent showed an increased tendency to buy from companies deemed to be such. Moreover, sixty-four percent of respondents claimed that environmental regulations greatly affected their brand loyalty. These findings emphasise the significance of adding sustainability into marketing plans to establish confidence in a company and to establish long-term ties with contented consumers. © 2025 IEEE.

关键词： Environmental regulations

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Exploring Few-Shot Learning to Enhance NLP's Cross-Domain Capabilities

Exploring Few-Shot Learning to Enhance NLP's Cross-Domain Ca...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： T. Suresh Balakrishnan Gururama Senthilvel. P U. Samson Ebenezar L. Karthikeyan Kishan B S Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Mechanical Engineering BMS Institute of Technology and Management Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Here, few shot learning (FSL) has been identified as a valuable method for improving the flexibility of NLP when dealing with new and different datasets with small amounts of training data. Most of the traditional NLP models are affected by shift since they are based on a big data set of a different domain. To overcome this challenge, FSL facilitates training of models to extrapolate from a few examples and not necessarily from numerous samples that may be present in LMMs. In this paper, I delve into meta-learning, prompt-based learning, and transfer learning as some of the methods that can be applied to boost NLP's cross-domain performance. We explain how these methods contribute to enhance performance in the use of fewer resources and evaluate the efficiency of those techniques on textual classification, sentiment analysis, and machine translation. Also, the pretrained GPT and BERT are beneficial to few-shot learning as it takes place through contextual learning. Based on our results, the conclusion is made that the application of FSL techniques in NLP pipelines enhances generalization and reduces the time and expenses on data annotation. Yet, some of the challenges that are associated with text analytics are data bias, sensibility of the prompts and computational problems. Finally, we propose some future works for FSL such as: the proposal of a new FSL model that combines traditional supervised and unsupervised learning; the presentation of a new FSL model based on a reinforcement learning theory. Hence, with the use of FSL, NLP models can perform well in different domains of application with little to no labeled data hence transferring knowledge to different domains to increase efficiency and scale in knowledge.

关键词： Metalearning Training Adaptation models Computational modeling Transfer learning Training data Data models Few shot learning Sustainable development Unsupervised learning

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Machine Learning Algorithms for Cloud-Based Educational Games: Engaging Students with Adaptive Learning Systems

Machine Learning Algorithms for Cloud-Based Educational Game...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Ravindar, B. Podduturi, Mahipal Reddy Basha, D Khalandar Mohan, Chinnem Rama Rokade, Geetanjali Popat Chakravarthi, M. Kalyan Sr University School of Cs & Ai Telangana Warangal India Solutions Architect Affiliation Penske Corporation DowningtownPA19335 United States Institute of Aeronautical Engineering Hyderabad500049 India Narayana Engineering College Department of Computer Science and Engineering Andhra Pradesh Nellore524004 India Maharashtra Pune India University of Technology and Applied Sciences Department of Electronics and Communication Engineering MuscatPO Box 74 Oman

ISBN: (纸本)9798331508685

The climate of contemporary instruction is changing because of the fast improvement of instructive methodologies. This review looks at a scope of new instructive innovations, including their purposes, benefits, hardships, and potential to reform regular homeroom settings. The research explores how understudy commitment, learning results, and showing approaches are impacted by innovations as well as man-made brainpower (simulated intelligence), virtual reality, augmented reality (AR), gamification, and versatile learning frameworks. The objective of the ongoing task is to decide the ideal calculation for anticipating homeroom investment from understudies. In this review, we gathered information from virtual learning environment (VLE) utilizing versatile learning and handled it utilizing a scope of information preprocessing strategies, like exception ID, standardization, encoding, and missing worth evacuation. Utilizing a scope of commonsense markers and cross-approval draws near, this paper surveyed various AI (ML) characterization frameworks on our information. The outcomes demonstrate that the CatBoost model outperforms the others in terms of accuracy. When compared to earlier studies, this suggested model performed better in every way. According to the paper's results section, the CatBoost model's accuracy was roughly 95.34%, its precision was 97.75%, and its recall was 95.4%. The AISAR model was contrasted with when compared to the AISAR 91% model, our model's precision of 97.75% indicates that our findings are superior. © 2025 IEEE.

关键词： Federated learning

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Traveltrek: Effortless Travel Crafting and Management

Traveltrek: Effortless Travel Crafting and Management

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Himanshu Pushpendra Singh Vinai Kumar Singh Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad India Department of Applied Science and Humanities GL BAJAJ Group of Institutions Mathura India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Today's rapidly changing world has led travelers to seek customized experiences that align with their individual preferences and interests. With the continuous evolution of the digital realm, the expectations of users interacting with online platforms for travel planning and reservations are also evolving. To cater to the demand for personalized recommendations and enhance the user experience, we propose the incorporation of a Content-Based Filtering algorithm into our tour website. This algorithm, in conjunction with a recommender system for Travel and tourism management, enables users to book tours nationwide through a single dynamic website that provides comprehensive information on various destinations and tour specifics. Developed with PHP as the front end and Microsoft SQL Server 2008 as the back end, the website is compatible with all browsers. Programming languages such as HTML, CSS, Bootstrap, and PHP were utilized in its creation. Administrators have the capability to add tour packages from selected travel agents and hotels by establishing a tour page. Subsequently, users can register and reserve each package, with confirmation managed by the admin in the booking management section. Users can easily view their confirmations in the “My Booking” page, making this platform user-friendly for all travelers seeking seamless bookings and detailed information. Furthermore, alongside implementing a Content-Based Filtering algorithm for personalized tour recommendations, we aim to promote the availability of accommo-dations in local rural areas. This endeavor not only presents travelers with cost-effective lodging choices but also contributes to the economic growth of the communities they visit.

关键词： Heuristic algorithms System performance Transportation Filtering algorithms User experience Planning Registers Servers Recommender systems Testing

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Optimization of Quantum Dilated Convolutional Neural Networks: Image Recognition With Quantum Computing

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Internet technology Letters 2025年第3期8卷

作者： Basha, Rahamat Pathak, Pankaj Sudha, M. Soumya, K.V. Arockia Venice, J. Department of Computer Science and Engineering Malla Reddy College of Engineering and Technology Telangana Hyderabad India Maharashtra Pune India Department of Information Technology Karpagam Institute of Technology Tamilnadu Coimbatore India Department of Electrical and Electronics and Engineering New Horizon College of Engineering Karnataka Bengaluru India DMI-ST.Eugene University Lusaka Zambia

As computer vision tasks increasingly rely on Convolutional Neural Networks (CNNs) with ever-expanding parameter counts, the need for computational resources for model training is growing unsustainable, surpassing traditional computing hardware's progress. To address this challenge, emerging paradigms such as quantum computing are gaining attention as prospective alternatives for the future. This manuscript proposes Quantum Dilated Convolutional Neural Networks Revolutionizing Image Recognition with Quantum Computing (QDCNN-IR-QC). The first step is to use the MNIST dataset for the input pictures. Subsequently, Improved Bilateral Texture Filtering (IBTF) is used to preprocess the input pictures. Subsequently, E-LBP is used to extract pertinent features from the preprocessed pictures. In most cases, E-LBP does not show that optimization methods for picture recognition have been adjusted. Therefore, in order to adjust the E-LBP weight parameter, this paper suggests an ISMO optimization approach. Lastly, a new quantum architecture for picture identification is developed using QDCNN. To implement the proposed approach, Python is used. This is where metrics like F-Measure, accuracy, sensitivity, specificity, and precision are assessed. When compared to current techniques such as QOCNN-IR-QC, ANN-IR-QC, and QKNN-IR-QC, the proposed approaches provide 5.27%, 7.21%, and 8.23% greater accuracy, respectively, in terms of efficiency. © 2025 John Wiley & Sons Ltd.

关键词： Qubits

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Quantum GA-driven Digital Twin for task urgency-aware partitioning and offloading in multi UAV-Aided MEC systems

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Ad Hoc Networks 2025年 176卷

作者： Ghosh, Santanu Kuila, Pratyay Department of Computer Science & Engineering National Institute of Technology Sikkim Sikkim Ravangla737139 India

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) empowers smart mobile devices (SMDs) to efficiently handle computation- and resource-intensive applications, particularly in critical scenarios. The integration of Digital Twin (DT) technology enhances scalability and streamlines the management of multi-user, multi-UAV-assisted MEC systems. This research focuses on partial task offloading within DT-enabled UAV-assisted MEC, addressing the joint problem of task partitioning and offloading using a quantum-inspired genetic algorithm (QIGA). The quantum chromosome is encoded and decoded through linear hashing. Task partitioning is performed to optimize system efficiency in terms of energy, latency, and load distribution across the MEC, while also considering task urgency. The fitness function incorporates two penalty factors to eliminate solutions that violate task deadlines or exceed the energy constraints of SMDs and edge servers. The QIGA is demonstrated to operate in polynomial time across all phases. Extensive simulations under various scenarios reveal that the proposed QIGA outperforms other algorithms in terms of energy efficiency, delay reduction, and load balancing within UAV-assisted MEC. Statistical analyses further validate the reliability and effectiveness of the results. © 2025 Elsevier B.V.

关键词： Drones

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AI-Powered NLP Systems for Differential Diagnosis: Improving COPD Exacerbation Detection

AI-Powered NLP Systems for Differential Diagnosis: Improving...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： K. Vishalatchi N. Thenmoezhi Shobica K Rajan. S P.T. Sivagurunathan M. Udhayamoorthi Department of Electronics and Communication Engineering AAA College of Engineering and Technology Sivakasi Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Department of Electronics and Communication Engineering Velalar College of Engineering and Technology Erode Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering Karur Department of Computer Science and Design Karpagam College of Engineering Coimbatore

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Chronic Obstructive Pulmonary Disease (COPD) exacerbation is a life-threatening condition which manifests with overlapping symptoms that require early and correct differential diagnosis to ensure effective treatment. To this end, in this study, we propose a Natural Language Processing (NLP) enabled AI system to enhance COPD exacerbation identification and classification from EHRs and clinical notes. Based on large language models (LLMs) and AI based multi agent architectures, we automate data extraction and analysis of patients' data such as symptoms, patient's medical background and lifestyle factors. We perform unstructured clinical text processing for the cases of COPD exacerbation vs other respiratory conditions using multiple machine learning classifiers and NLP pipelines. It combines structured and unstructured data sources to increase the precision of conjunctive diagnostics and thus shorten the time needed for medical professionals to assess critical cases. In the study, different AI models are evaluated by random forest, logistic regression and Naïve Bayes classifiers, and results indicate that the random forest model is able to detect COPD exacerbation with the highest accuracy (80%) and F1-score (73%). This AI powered NLP system can automate the Medical History analysis and differential diagnosis process to be more efficient in diagnosis, reduce misclassification and improve patient outcomes. The model will be developed to include real-time patient monitoring and has been integrated with hospital information systems for clinical decision supports.

关键词： Accuracy Large language models Feature extraction Natural language processing Chronic obstructive pulmonary disease Differential diagnosis Real-time systems Artificial intelligence Medical diagnostic imaging Random forests

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EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Toward Large-Scale EEG Model

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2025年 33卷 1653-1663页

作者： Qin, Chengxuan Yang, Rui You, Wenlong Chen, Zhige Zhu, Longsheng Huang, Mengjie Wang, Zidong Xi’an Jiaotong-Liverpool University School of Advanced Technology Suzhou215123 China University of Liverpool School of Electrical Engineering Electronics and Computer Science LiverpoolL69 3BX United Kingdom The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong Xi’an Jiaotong-Liverpool University Design School Suzhou215123 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom

The increasing number of dispersed EEG dataset publications and the advancement of large-scale Electroencephalogram (EEG) models have increased the demand for practical tools to manage diverse EEG datasets. However, the inherent complexity of EEG data, characterized by variability in content data, metadata, and data formats, poses challenges for integrating multiple datasets and conducting large-scale EEG model research. To tackle the challenges, this paper introduces EEGUnity, an open-source tool that incorporates modules of "EEG Parser", "Correction", "Batch Processing", and "Large Language Model Boost". Leveraging the functionality of such modules, EEGUnity facilitates the efficient management of multiple EEG datasets, such as intelligent data structure inference, data cleaning, and data unification. In addition, the capabilities of EEGUnity ensure high data quality and consistency, providing a reliable foundation for large-scale EEG data research. EEGUnity is evaluated across 25 EEG datasets from different sources, offering several typical batch processing workflows. The results demonstrate the high performance and flexibility of EEGUnity in parsing and data processing. © 2001-2011 IEEE.

关键词： Data integration

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