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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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Word-of-Mouth Promotion: How to Attract Consistent Consumers as a Promoter for the B2C Model 2

Word-of-Mouth Promotion: How to Attract Consistent Consumers...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Gunavathi, R. Vijayalakshmi, S. Savithri, M. Thamarai Selvi, S.B. Christ University School of Data Science India K.S.Rangasamy College of Technology Computer Science and Engineering India

ISBN: (纸本)9798350384277

The primary goal of this research article is to discover the consumers' behaviour while spending time on the e-commerce platform and to use the consumers who have positive Word-of-Mouth on the products to motivate them as promoters through positive Word of Mouth behaviour. The behavioural factors considered in this study are Relationship value, Trust value, Satisfaction level and Word of Mouth. The trial model includes the consumers who use an e-commerce platform for their online shopping in India. A proper questionnaire with four components was created and used to collect the sample data. Totally 300 responses have been received and analysed with the help of structured equation model and SPSS and AMOS software. The findings suggest that the 'Word of Mouth' technique can be used as a tool to increase the number of consumers in an online platform, particularly e-commerce. We investigated how Relationship Value and Trust Value can be used as key factors to motivate consumers' positive WoM behaviour. This research would be more beneficial to the B2C model. The research has done only for Indian e-commerce portals for survey. There is a scope to do research for the global level e-commerce market. Future study focuses on dynamic attributes for relationship values. This research work will help the researchers who is working on B2C model and consumer behavioural models. This model would be used for any online transactions-based services. As best of the knowledge of the authors' this study is the novel idea to understand the consumers' behaviour for purchasing items through the positive WoM. This work can be adopted for any e-commerce platform. © 2024 IEEE.

关键词： Marketplaces

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Revisiting 30 years of the Network Time Protocol 24

Revisiting 30 years of the Network Time Protocol

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Woo, Simon S. Computer Science and Engineering Department Applied Data Science Department Department of Artificial Intelligence Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798400701726

Since the inception of the Internet and WWW, providing the time among multiple nodes on the Internet has been one of the most critical challenges. David Mills is the pioneer to provide time on the Internet, inventing the Network Time Protocol (NTP), and synchronizing the clocks in computer systems. Now, the NTP is predominantly used on the Internet and WWW. In this paper, we revisit the NTP, and present the overview of the NTP. In particular, we highlight the advanced research effort, the SpaceNTP, to synchronize the clocks among assets and entities in space. The SpaceNTP designed for space environments will be the fundamental medium and enabling block to provide the future web services in space. © 2024 Copyright held by the owner/author(s).

关键词： Web services

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Drawing Order Diagrams Through Two-Dimension Extension

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Journal of Graph Algorithms and Applications 2023年第9期27卷 783-802页

作者： Dürrschnabel, Dominik Hanika, Tom Stumme, Gerd Knowledge & Data Engineering Group Research Center for Information System Design & Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Computer Science University of Hildesheim Germany Berlin School of Library and Information Science Humboldt-Universität zu Berlin Germany

Ordinal real-world data such as concept hierarchies, ontologies, genealogies, or task dependencies in scheduling often has the property to not only contain pairwise comparable, but also incomparable elements. Order diagrams provide an important tool for understanding and thus discovering knowledge in such data. Easily readable drawings of such order diagrams are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work, we present the novel algorithm DimDraw to decompose an ordered set (e.g., a concept hierarchy) in linear orders and to produce a corresponding order diagram. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of its transitive incompatibility graph. To evaluate the quality of the algorithm, a user study was conducted where generated drawings were compared with ones from state-of-the-art drawing algorithms. © 2023 Brown University. All rights reserved.

关键词： Set theory

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Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated IoT Prediction 3

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Varun, P. Sunitha, T. Nagalingam, Mythili Nithiya, P. Selvakumaran, S. Mohanaprakash, T.A. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai600119 India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities. © 2024 IEEE.

关键词： Solar power generation

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Ensemble of deep learning models with Walrus Optimization Algorithm for accurate botnet recognition and classification

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Iran Journal of computer science 2025年 1-18页

作者： Chacko, Ashwathy Anda Edwin, E. Bijolin Thanka, M. Roshni Ebenezer, V. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

The rapid growth of Internet of Things (IoT) networks has introduced significant security challenges, with botnet attacks being one of the most prevalent threats. These attacks exploit vulnerabilities in IoT devices, leading to severe disruptions and damage to critical infrastructures. Detecting botnet attacks in IoT environments is challenging due to the large volume of data, the dynamic nature of traffic, and the diverse attack patterns. To address these issues, we propose a novel approach called Walrus Optimized Ensemble Deep Learning for Anomaly-Based Recognition Classifier (WOAEDL-ABRC), which leverages a combination of advanced machine learning techniques for effective botnet detection. The methodology of this research involves four key components: (1) data preprocessing through min–max normalization to scale the features appropriately, (2) feature selection using the social cooperation search algorithm (SCSA) to identify the most informative attributes, (3) an ensemble deep learning model combining convolutional autoencoder (CAE), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) for robust anomaly detection, and (4) hyperparameter optimization using the Walrus Optimization Algorithm (WAOA), which fine-tunes the model parameters for optimal performance. This ensemble approach ensures that the model benefits from the strengths of each individual technique while mitigating the weaknesses of others. The dataset used for this research includes network traffic data from IoT environments, consisting of various botnet attack scenarios and normal traffic patterns. The data undergoes extensive preprocessing and feature selection to reduce dimensionality and enhance the model’s performance. The implementation is carried out in Python using TensorFlow for deep learning, with the WAOA applied to optimize hyperparameters. The results demonstrate the effectiveness of the WOAEDL-ABRC in detecting botnet attacks, achieving superior accuracy, precision

关键词： Botnet recognition Deep learning Feature selection Internet of things Walrus Optimization Algorithm

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An Architecture for Dynamic Load Balancing in Cloud Environment 2

An Architecture for Dynamic Load Balancing in Cloud Environm...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Lokesh, Gudivada Baseer, K.K. Dept. of Computer Science and Engineering Tirupati India Department of Data Science Tirupati India

ISBN: (纸本)9798350347579

Clouds are highly customizable infrastructures that offer a platform as a service and let customers subscribe on a pay-as-you-go basis to their requirements. The straightforward service-oriented cloud computing model is gaining popularity around the world. The number of people using the Cloud is constantly growing. Clouds use modern data centers to manage a massive number of users. The reliability of the Cloud depends on load balancing. Balancing virtual machine loads lowers energy consumption and task rejections by optimizing resource utilization. One can increase performance while using fewer resources using load balancing, resource management, quality of service, etc. The difficulty of overloading and underloading virtual machines in cloud computing can be lessened by load balancing in the *** research study thoroughly examined the load-balancing algorithms found in the literature. First, the traditional approaches are analyzed before moving on to more recent work on load balancing with heterogeneous techniques. Along with the tools available for the current investigation, various metrics are used to evaluate the load-balancing algorithms. The proposed article will primarily serve to assist in the development of new algorithms in the future. © 2023 IEEE.

关键词： Energy utilization

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ReCSA:a dedicated sort accelerator using ReRAM-based content addressable memory

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Frontiers of computer science 2023年第2期17卷 1-13页

作者： Huize LI Hai JIN Long ZHENG Yu HUANG Xiaofei LIAO National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

With the increasing amount of data,there is an urgent need for efficient sorting algorithms to process large data *** sorting algorithms have attracted much attention because they can take advantage of different hardware's *** the traditional hardware sort accelerators suffer“memory wall”problems since their multiple rounds of data transmission between the memory and the *** this paper,we utilize the in-situ processing ability of the ReRAM crossbar to design a new ReCAM array that can process the matrix-vector multiplication operation and the vector-scalar comparison in the same array *** this designed ReCAM array,we present ReCSA,which is the first dedicated ReCAM-based sort *** hardware designs,we also develop algorithms to maximize memory utilization and minimize memory exchanges to improve sorting *** sorting algorithm in ReCSA can process various data types,such as integer,float,double,and *** also present experiments to evaluate the performance and energy efficiency against the state-of-the-art sort *** experimental results show that ReCSA has 90.92×,46.13×,27.38×,84.57×,and 3.36×speedups against CPU-,GPU-,FPGA-,NDP-,and PIM-based platforms when processing numeric data *** also has 24.82×,32.94×,and 18.22×performance improvement when processing string data sets compared with CPU-,GPU-,and FPGA-based platforms.

关键词： ReCAM parallel sorting architecture design processing-in-memory

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Layer-Wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-Trained Models: An Evolutionary Approach

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-23页

作者： Bu, Chenyang Liu, Yuxin Huang, Manzong Shao, Jianxuan Ji, Shengwei Luo, Wenjian Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

The superior performance of large-scale pre-Trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-Trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-Tuning or transfer learning in subsequent tasks. After pre-Training is complete, a small amount of labeled data can be used to fine-Tune the model for a specific task or domain. This two-stage method of "pre-Training+fine-Tuning"has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-Tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-Tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-Tuning patterns for specific NLP tasks than typical fixed fine-Tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-Training language models show the effectiveness and generality of the proposed framework. © 2024 held by the owner/author(s).

关键词： Semantics

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MDNN:Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning

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computer Modeling in engineering & sciences 2023年第7期136卷 381-401页

作者： Yi Chen Jin Zhou Qianting Gao Jing Gao Wei Zhang School of Computer Science Central China Normal UniversityWuhan430079China Computer Science School of ScienceRensselaer Polytechnic InstituteTroy12180USA National Engineering Laboratory of Educational Big Data Application Technology Central China Normal UniversityWuhan430079China

Prediction of students’engagement in aCollaborative Learning setting is essential to improve the quality of *** learning is a strategy of learning through groups or *** cooperative learning behavior occurs,each student in the group should participate in teaching *** showed that students who are actively involved in a class gain *** behavior and facial expression are important nonverbal indicators to reveal engagement in collaborative learning *** studies require the wearing of sensor devices or eye tracker devices,which have cost barriers and technical interference for daily teaching *** this paper,student engagement is automatically analyzed based on computer *** tackle the problem of engagement in collaborative learning using a multi-modal deep neural network(MDNN).We combined facial expression and gaze direction as two individual components of MDNN to predict engagement levels in collaborative learning *** multi-modal solution was evaluated in a real collaborative *** results show that the model can accurately predict students’performance in the collaborative learning environment.

关键词： Engagement facial expression deep network gaze

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