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检索条件"任意字段=IEEE International Conference on Cloud Computing Technology and Science"

共 110967 条记录，以下是41-50 订阅

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Proposed AES-based Obfuscation and Encryption Model for cloud Data Security 1

Proposed AES-based Obfuscation and Encryption Model for Clou...

引用

1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Singh, Prabhdeep Singh, Pawan Agarwal, Abhay Kumar Kumar, Anil Amity School of Engineering and Technology Department of Computer Science and Engineering Lucknow India Kamla Nehru Institute of Technology Department of Computer Science and Engineering Sultanpur India Amity University Amity School of Engineering and Technology Lucknow India

ISBN: (纸本)9798331529246

cloud computing (CC) offers a diverse range of services along with huge data storage across a network. CC has collaborated with varied emerging technologies like IoT because of its numerous advantages. Despite CC's benefits, cloud-based servers are not always trustworthy in real-world scenarios. Users' privacy will be compromised if they send their data straight to cloud platforms since crucial information will be exposed to cloud servers. Thereby, this work develops a data security model in the cloud with two stages, proposed secured data storage and proxy re-encryption. Initially, the data is stored securely in the cloud based on improved AES-based encryption as well as obfuscation-based encryption. Based on the request from the data user, proxy re-encryption is done using Kronecker product-based encryption. The enhanced AES achieved the lowest value of 0.025 for the CCA attack with a key size of 128 bits. Thus, the final encoded key is obtained. © 2025 ieee.

关键词： cloud platforms

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Evaluating the Necessity of cloud computing in Modern Industrial Applications: A Comprehensive Analysis 2

Evaluating the Necessity of Cloud Computing in Modern Indust...

引用

2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Singh, Abhiraj Soni, Manish Kumar Pratap, Ajay Dept.of Computer Sci. & Engineering Bansal Inst of Eng. & Technology Lucknow India Amity Institute of Info Technology Amity University Uttar Pradesh Lucknow India

ISBN: (纸本)9798331530389

This study thoroughly assesses the need for cloud computing across industries, examining the ways in which it improves scalability, flexibility, and affordability. Through the use of cloud computing, industries may update their IT infrastructure and move from outdated, capital-intensive models to more adaptable and effective cloud-based solutions. The report examines how cloud services like Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are integrated into key industries like manufacturing, healthcare, finance, and retail. Through the implementation of cutting-edge technologies like Internet of Things (IoT), machine learning (ML), and artificial intelligence (AI), cloud computing stimulates innovation, optimizes operations, and improves decision-making via data-driven insights. The growing significance of hybrid and multi-cloud strategies-which offer increased flexibility and enable industries to optimize resources and enhance performance-is also covered in the study. The study discusses the difficulties that sectors encounter in implementing cloud computing, along with its advantages. These difficulties include worries about data security, regulatory compliance, and cloud management. Strong security frameworks and efficient governance models are highlighted as essential elements of successful cloud integration. This study presents a framework for adopting cloud-based solutions for industries and emphasizes the significance of cloud computing as a fundamental technology for contemporary industrial applications. © 2025 ieee.

关键词： cloud platforms

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The interactive impact and integration of AI, big data, and cloud computing combined with photonics technology 2

The interactive impact and integration of AI, big data, and ...

引用

2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zeng, Changxi Xu, Yixuan Yang, Lei School of Computer Science Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781510689053

This article aims to analyze the interactive impact and integration development trend of three technologies: artificial intelligence, big data, and cloud computing. Overview of its basic concepts and technical characteristics, analysis of its interconnections and impacts, the development of AI technology relies on the support of big data, and cloud computing provides powerful infrastructure and computing capabilities for the application of AI and big *** optical and photonics technologies have also played an important role in this fusion process, particularly in terms of data security and privacy *** analyzing the current integration status of these technologies in practical applications, such as intelligent transportation management, personalized medical and health services, further discussions were held on the challenges encountered in the process of technology integration, such as data security and privacy protection, and future development directions were also discussed. The analysis results indicate that the deep integration of AI, big data, and cloud computing will bring more innovation opportunities and development momentum to various industries, while also emphasizing the importance of strengthening interdisciplinary cooperation and improving laws and regulations to ensure that technological progress is balanced with social ethics and public interests. © 2025 SPIE.

关键词： Electronic health record

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cloud Based IDS-Anomaly Detection Using Machine Learning 1

Cloud Based IDS-Anomaly Detection Using Machine Learning

引用

1st international conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Rachani, Raj Mahendrakumar Ravish, Roopa P E S University Department of Computer Science Bangalore India

ISBN: (纸本)9798350355611

As organizations increasingly rely on cloud computing for their critical operations, securing cloud infrastructures against cyber threats has become paramount. This paper presents a robust and scalable Intrusion Detection System (IDS) designed for cloud environments, leveraging machine learning techniques for efficient anomaly detection. The proposed system aims to enhance the detection capabilities of traditional IDS by harnessing the power of diverse machine learning models within a cloud-based architecture. The machine learning approach combines multiple anomaly detection algorithms, each contributing its unique perspective, to form a comprehensive and adaptive defense mechanism. Leveraging cloud resources enables the system to dynamically scale its computational power based on demand, ensuring real-time monitoring and response to emerging threats. The IDS utilizes a combination of feature engineering and extraction methods to process large-scale data generated by cloud applications, allowing for effective anomaly detection without compromising system performance. © 2025 ieee.

关键词： Adversarial machine learning

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Industrial IoT Smart Manufacturing Systems based on cloud computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 ieee.

关键词： Smart manufacturing

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Design of Automated Container Layout and Resource Optimization Algorithm based on cloud computing technology

Design of Automated Container Layout and Resource Optimizati...

引用

2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhang, Feng Shandong Province Light Industry Collective Enterprise Association Information Department Shandong Jinan China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

With the rapid development and widespread application of cloud computing, container technology has become a hot topic for enterprises and research institutions in recent years. This article proposes an automated container arrangement and resource optimization algorithm design based on cloud computing technology to address these issues. This algorithm can predict and adjust resources based on real-time and historical data of applications, ensuring efficient resource utilization and good application performance. The experimental results show that our optimization algorithm has improved by about 16% compared to the optimal adaptation strategy. Due to the more precise resource allocation of the algorithm in this article, the response time of the application has not been significantly affected. In the testing of system throughput, it can be found that our algorithm has improved throughput by about 15% compared to random strategies. Finally, energy consumption was tested and the results showed that our algorithm reduced energy consumption by approximately 28% due to higher resource utilization. These results demonstrate that our algorithm outperforms existing strategies in multiple key metrics. © 2025 SPIE.

关键词： cloud platforms

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Maintaining Privacy in Contact Tracing Systems for Pandemics Through Edge computing 1

Maintaining Privacy in Contact Tracing Systems for Pandemics...

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1st international conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Tejaswini, Virupakshi Sirisala, NageswaraRao K.S.R.M. College of Engineering Department of Computer Science and Engineering A.P Kadapa India

ISBN: (纸本)9798350355611

Data processing and publication are crucial today with the advancements in technologies such as edge computing and IoT(Internet of Things). A vast volume of data is being generated by internet-connected devices, often on a continuous basis. The COVID-19 pandemic data exemplifies this trend of constant data flow, as the global medical emergency has necessitated ongoing data collection. This data must be processed and analyzed, and subsequently published, while ensuring personal privacy is upheld. Edge computing devices can handle various tasks like data preprocessing and analysis in any setting. It is crucial to process data before uploading it to the cloud and sharing it with advisories or media outlets, as improper handling could lead to privacy breaches. To protect personal privacy, measures such as differential privacy techniques are implemented to protect data. Edge computing technologies offer several advantages, including efficient data processing close to the origin to minimize internet data transmission capacity use and enhanced security for confidential information on public cloud infrastructures. © 2025 ieee.

关键词： Data Sharing Differential Privacy (DP) Edge computing Individual Privacy

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cloud computing in Enhancing the Efficiency and Integration of Alternative Energy Systems 2

Cloud Computing in Enhancing the Efficiency and Integration ...

引用

2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sahoo, Amiya Kumar Prusty, Sucharita Jayasingh, Suvendra Kumar Swain, Asish Kumar Mohanta, Hemanta Kumar Aryan Institute of Engineering and Technology Odisha Bhubaneswar India Institute of Management and Information Technology BPUT Odisha Cuttack India

ISBN: (纸本)9798331530389

In response to growing environmental concerns and the need for sustainable development, alternative energy systems have emerged as crucial solutions to the problems associated with fossil fuels. This research focuses on the significant role cloud computing plays in improving the efficiency and integration of various alternative energy systems such as solar, wind, hydropower, biomass, geothermal, and tidal/wave energy. The study explores how cloud computing technologies enable real-time data processing, storage, and analysis of the large datasets generated by these renewable energy sources, thus optimizing their performance and integration into the energy grid. cloud computing provides advanced data management tools that are vital for managing the variable and intermittent nature of renewable energy production. By utilizing cloud-based platforms, energy producers can achieve improved forecasting, load balancing, and energy distribution, which are essential for enhancing efficiency and reliability. Additionally, this research highlights how cloud computing fosters collaboration and innovation by offering a scalable and flexible infrastructure. This infrastructure supports the development of new technologies and applications within the renewable energy sector, promoting continuous advancements and breakthroughs. This research underscores the crucial role of cloud computing in advancing a sustainable energy future. © 2025 ieee.

关键词： cloud platforms

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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in cloud computing 1

A Hybrid Approach Using Cryptography and K-Least Significant...

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1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Goyal, Anshika Gupta, Bhumika VMSB Uttarakhand Technical University Dept. of Computer Science & Engineering Dehradun India G.B. Pant Institute of Engineering & Technology Dept. of Computer Science & Engineering Pauri India

ISBN: (纸本)9798331518578

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in cloud computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT). © 2025 ieee.

关键词： cloud platforms

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Improving cloud Efficiency: A Machine Learning-Based Stacking Model for CPU Utilization Prediction 8

Improving Cloud Efficiency: A Machine Learning-Based Stackin...

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8th international conference on Data science and Machine Learning Applications, CDMA 2025

作者： Javeed, Ashir Borg, Anton Grahn, Hakan Lundberg, Lars Patel, Dhyey Shirinbab, Sogand Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden Ericsson AB Karlskrona Sweden

ISBN: (纸本)9798331539696

With the rapid growth of internet technologies, IT businesses are transferring to cloud-based systems, and cloud-based services are in high demand among internet users. Therefore, appropriate allocation of resources in cloud computing environments is essential. The companies can reduce costs by saving energy by dynamically scaling up or down the number of active servers. In this context, this study presents a machine learning-based model for accurate prediction of CPU utilization. Previous studies employed timestamp-based data to predict CPU utilization in cloud computing, while the proposed work uses incoming user requests to predict CPU workload so that a timely decision can be made to scale up or scale down the servers in a cloud computing environment. The proposed model is based on several machine learning algorithms that are stacked into a single model called the stacking model for CPU workload prediction. The effectiveness of the proposed stacking model was tested on several evaluation metrics to validate its performance. Furthermore, the performance of the proposed stacking model is also compared with other state-of-the-art machine learning models such as support vector machines (SVM), decision trees (DT), random forests (RF), gradient boosting, and extreme gradient boosting (XGBoost). © 2025 ieee.

关键词： Prediction models

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