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An anonymous and privacy-preserving lightweight authentication protocol for secure communication in UAV-assisted IoAV networks

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computer Communications 2025年 239卷

作者： Shariq, Mohd Jamil, Norziana Rawat, Gopal Singh Chaudhry, Shehzad Ashraf Masud, Mehedi Cangelosi, Angelo Department of Information Systems and Security College of IT UAE University P.O.Box 15551 Al Ain Abu Dhabi United Arab Emirates Bennett University Greater Noida201310 India Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul34398 Turkey Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Manchester Centre for Robotics and AI University of Manchester United Kingdom

With the rapid proliferation of the Internet of Things (IoT), autonomous vehicles (AVs), or self-driving cars, rely heavily on real-time data sharing and message exchanges over wireless networks. AVs use sensors, artificial intelligence, machine learning, and advanced algorithms to perform various functions, enabling users to operate without human intervention. Owing to the flexibility and high mobility of drones, they could aid in the operations of AVs. However, the security and privacy are the main concerns;specifically, the threat of physical capture and violation of anonymity are the main hurdles for realization of secure communication among the AVs and drones. To address these challenges, we propose an anonymous and provably Secure Lightweight Authentication Protocol for unmanned-aerial-vehicle-assisted Internet of Autonomous Vehicles (SLAP-IoAV). The proposed protocol uses cryptographic primitives such as exclusive-OR operations, elliptic-curve cryptography, collision-resistant one-way hashing, and concatenation to ensure robust security. An informal security analysis found that SLAP-IoAV is secure against several known attacks, and a performance analysis established that the protocol has less computational and communication overhead than existing competitive protocols. Additionally, Scyther simulation results confirm that no security vulnerabilities are present. Overall, our protocol delivers superior security and performance, making it well-suited to real-world applications in the AV industry. © 2025 Elsevier B.V.

关键词： Authentication Protocol

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Development of a deep learning system for preventive detection of heart failure due to pulmonary disease

Development of a deep learning system for preventive detecti...

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International Conference on software Process Improvement (CIMPS)

作者： Axel Frederick Félix Jiménez Isaul Ibarra Belmonte Vania Stephany Sánchez Lee Uziel Jaramillo Avila Ezra Federico Parra González Computer Systems Engineering National Polytechnic Institute Zacatecas México Software Engineer Center for Research in Mathematics Zacatecas Zacatecas México Computer Science Department Center for Research in Mathematics Zacatecas Zacatecas México

ISBN: (纸本)9798350398977

In the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the development of severe disease efficiently. Our approach employs the use of medical data retrieved from the user to determine and predict whether there is a likelihood of a potential condition. To solve this problem, according to a users medical measurement history, a deep learning model can be implemented to determine a preventive diagnosis or otherwise to follow up on an already detected condition. By posing the problem as a classification task, it can be taken advantage of a deep learning model focused on heart failure or pulmonary conditions to make a preliminary diagnosis and determine if there are signs of any symptomatology.

关键词： Cardiovascular diseases Diseases Deep learning Medical diagnostic imaging Lung Prediction algorithms Medical services

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Panel Session: A Vision for the Next 15 Years of Computing Education

Panel Session: A Vision for the Next 15 Years of Computing E...

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Frontiers in Education (FIE) Conference

作者： Adrienne Decker Monica M. McGill Briana Morrison Manuel A. Pérez-Quiñones Aman Yadav Department of Engineering Education University at Buffalo Buffalo NY USA Institute for Advancing Computing Education Peoria IL USA Computer Science University of Virginia Charlottesville VA USA Software and Information Systems University of North Carolina Charlotte Charlotte NC USA College of Education and College of Natural Science Michigan State University East Lansing MI USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

The session will present the final report of an NSF sponsored workshop tasked with creating a vision for computing education for the next 15 years. Within several broad themes identified, the panelists were asked to probe further into where the field should be heading and what difficult questions we need to tackle. We will use the final report as a basis for discussion with the attendees and ask them the same hard questions we asked ourselves when writing this report to see how these questions shape their views of where computing education needs to be in the next 15 years.

关键词： Shape Conferences Education Writing Probes

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A Study on the Development of a Crop Disease Diagnosis Mobile App Service Based on Deep Learning 14th

A Study on the Development of a Crop Disease Diagnosis Mobil...

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14th International Conference on computer science and its Applications, CSA 2022 and the 16th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2022

作者： Lee, Dong In Lee, Ji Hwan Jung, Hoon Seo Jang, Seung Ho Ryu, Seung Gi Yang, Jiann An Oh, Ji Hwan Kim, Jae Won Oh, Se Jong Doo, Ill Chul Computer and Electronic Systems Engineering Hankuk University of Foreign Studies Yongin Korea Republic of Linguistics and Cognitive Science Hankuk University of Foreign Studies Yongin Korea Republic of School of Computer Science and Engineering Soongsil University Seoul Korea Republic of English-Korean Interpretation and Translation Studies Hankuk University of Foreign Studies Yongin Korea Republic of Department of Brazilian Studies Hankuk University of Foreign Studies Yongin Korea Republic of Division of French Language Hankuk University of Foreign Studies Seoul Korea Republic of International Finance Hankuk University of Foreign Studies Yongin Korea Republic of Statistics University of St Andrews St Andrews United Kingdom Artificial Intelligence Education Hankuk University of Foreign Studies Yongin Korea Republic of

ISBN: (纸本)9789819912513

We introduced a crop disease diagnosis mobile application service that improves the weakness of the previous services. We utilized image captioning model with Inception V3-based encoder and transformer-based decoder for detailed explanation. Moreover, object detection model with YOLOv5 was used to display bounding boxes to indicate the damaged region to increase the reliability. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

SmartEdge: Smart Healthcare End-to-End Integrated Edge and C...

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IEEE International Conference on Cloud Computing Technology and science (CloudCom)

作者： Alain Hennebelle Qifan Dieng Leila Ismail Rajkumar Buyya Independent Researcher in Information Technology Systems and Artificial Intelligence Applications Abu Dhabi United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Department of Computer Science and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of Information Technology Emirates Center for Mobility Research National Water and Energy Center UAE University Al-Ain Abu Dhabi United Arab Emirates

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction.

关键词： Cloud computing Accuracy Smart cities Computational modeling Transportation industry Surveillance Machine learning Predictive models Diabetes Time factors

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Towards Evidence-Based Cybersecurity Assessment of Programmable systems to Ensure the Protection of Critical IT Infrastructure

Towards Evidence-Based Cybersecurity Assessment of Programma...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications

作者： Oleg Illiashenko Vyacheslav Kharchenko Oleg Odarushchenko Department of computer systems and networks National aerospace university “KhAI” Kharkiv Ukraine Software Engineering & Dependable Computing Laboratory Institute of Information Science and Technologies “Alessandro Faedo”—ISTI CNR Pisa Italy Research and Production Enterprise RPC Radics LLC Kropyvnytskyi Ukraine

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature – physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very High-Speed Integrated Circuits Hardware Description language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems.

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Merging synthetic and real embryo data for advanced AI predictions

arXiv

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arXiv 2024年

作者： Presacan, Oriana Dorobanţiu, Alexandru Thambawita, Vajira Riegler, Michael A. Stensen, Mette H. Iliceto, Mario Aldea, Alexandru C. Sharma, Akriti Faculty of Electronics Telecommunications and Information Technology National University of Science and Technology Politehnica Bucharest Bucharest061071 Romania Department of Computer Science and Electrical Engineering Lucian Blaga University of Sibiu Sibiu550024 Romania Department of Holistic Systems SimulaMet Stensberggata 27 Oslo0170 Norway Department of Computer Science Oslo Metropolitan University Oslo0167 Norway Volvat Spiren Oslo0176 Norway Faculty of Biotechnologies University of Agronomic Sciences and Veterinary Medicine Bucharest011464 Romania Department of Validation Intelligence for Autonomous Software Systems Simula Research Laboratory Oslo0164 Norway Cyber Security Simula Research Laboratory Oslo0164 Norway

Accurate embryo morphology assessment is essential in assisted reproductive technology for selecting the most viable embryo. Artificial intelligence has the potential to enhance this process. However, the limited availability of embryo data presents challenges for training deep learning models. To address this, we trained two generative models using two datasets—one we created and made publicly available, and one existing public dataset—to generate synthetic embryo images at various cell stages, including 2-cell, 4-cell, 8-cell, morula, and blastocyst. These were combined with real images to train classification models for embryo cell stage prediction. Our results demonstrate that incorporating synthetic images alongside real data improved classification performance, with the model achieving 97% accuracy compared to 94.5% when trained solely on real data. This trend remained consistent when tested on an external Blastocyst dataset from a different clinic. Notably, even when trained exclusively on synthetic data and tested on real data, the model achieved a high accuracy of 92%. Furthermore, combining synthetic data from both generative models yielded better classification results than using data from a single generative model. Four embryologists evaluated the fidelity of the synthetic images through a Turing test, during which they annotated inaccuracies and offered feedback. The analysis showed the diffusion model outperformed the generative adversarial network, deceiving embryologists 66.6% versus 25.3% and achieving lower Fréchet inception distance scores. © 2024, CC BY-SA.

关键词： Generative adversarial networks

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DECODING EEG SPEECH PERCEPTION WITH TRANSFORMERS AND VAE-BASED DATA AUGMENTATION

arXiv

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arXiv 2025年

作者： Chen, Terrance Yu-Hao Chen, Yulin Soederhaell, Pontus Agrawal, Sadrishya Shapovalenko, Kateryna Computer Science Department Carnegie Mellon University PittsburghPA15213 United States Information Networking Institute Carnegie Mellon University PittsburghPA15213 United States Computational Finance Carnegie Mellon University PittsburghPA15213 United States Software and Societal Systems Department Carnegie Mellon University PittsburghPA15213 United States Language Technologies Institute Carnegie Mellon University PittsburghPA15213 United States

Decoding speech from non-invasive brain signals, such as electroencephalography (EEG), has the potential to advance brain-computer interfaces (BCIs), with applications in silent communication and assistive technologies for individuals with speech impairments. However, EEG-based speech decoding faces major challenges, such as noisy data, limited datasets, and poor performance on complex tasks like speech perception. This study attempts to address these challenges by employing variational autoencoders (VAEs) for EEG data augmentation to improve data quality and applying a state-of-the-art (SOTA) sequence-to-sequence deep learning architecture, originally successful in electromyography (EMG) tasks, to EEG-based speech decoding. Additionally, we adapt this architecture for word classification tasks. Using the Brennan dataset, which contains EEG recordings of subjects listening to narrated speech, we preprocess the data and evaluate both classification and sequence-to-sequence models for EEG-to-words/sentences tasks. Our experiments show that VAEs have the potential to reconstruct artificial EEG data for augmentation. Meanwhile, our sequence-to-sequence model achieves more promising performance in generating sentences compared to our classification model, though both remain challenging tasks. These findings lay the groundwork for future research on EEG speech perception decoding, with possible extensions to speech production tasks such as silent or imagined *** Codes 68T07, 92C55 Copyright © 2025, The Authors. All rights reserved.

关键词： Brain computer interface

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Research of the Particle Swarm Method for Generating Nonlinear Substitutions

Research of the Particle Swarm Method for Generating Nonline...

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2021 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics, UkrMiCo 2021

作者： Kuznetsov, Alexandr Myroshnychenko, Volodymyr Derevianko, Yaroslav Bulhakova, Olha Department of information systems and technologies security V. N. Karazin Kharkiv National University Kharkiv Ukraine Department of Economic and Information Security Dnipropetrovsk State University of Internal Affairs Dnipro Ukraine Department of Computer Science and Software Engineering University of Customs and Finance Dnipro Ukraine

ISBN: (纸本)9781665426527

Nonlinear substitutions (S-boxes) have an important role in ensuring security of symmetric cryptographical transformations. In this article, we consider a computational particle swarm optimization method (PSO) to generate S-boxes. We have implemented two algorithms for generating substitutions that reproduce a certain simulation of particle motion in the space of possible states to make experimental research. We perform a computational search and show the efficiency of these two implementations. Our proposed improved implementation allows us to confidently generate Sboxes with nonlinearity 104, delta uniformity 8, linear redundancy 0 and algebraic immunity 3. © 2021 UkrMiCo 2021 - 2021 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics, Proceedings. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol

arXiv

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arXiv 2023年

作者： Wang, Zhaorui Liu, Ya-Feng Wang, Ziyue Liu, Liang Pan, Haoyuan Cui, Shuguang The Future Network of Intelligence Institute The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices K. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of K is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for K and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of K are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of K and the design of the ADC quantizer;2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol. © 2023, CC BY.

关键词： Stochastic systems

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