Multi-level authentication for security in cloud using improved quantum key distribution.

IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Network-Computation in Neural Systems Pub Date : 2024-07-08 DOI:10.1080/0954898X.2024.2367480
Ashutosh Kumar, Garima Verma
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引用次数: 0

Abstract

Cloud computing is an on-demand virtual-based technology to develop, configure, and modify applications online through the internet. It enables the users to handle various operations such as storage, back-up, and recovery of data, data analysis, delivery of software applications, implementation of new services and applications, hosting websites and blogs, and streaming of audio and video files. Thereby, it provides us many benefits although it is backlashed due to problems related to cloud security like data leakage, data loss, cyber attacks, etc. To address the security concerns, researchers have developed a variety of authentication mechanisms. This means that the authentication procedure used in the suggested method is multi-levelled. As a result, a better QKD method is offered to strengthen cloud security against different types of security risks. Key generation for enhanced QKD is based on the ABE public key cryptography approach. Here, an approach named CPABE is used in improved QKD. The Improved QKD scored the reduced KCA attack ratings of 0.3193, this is superior to CMMLA (0.7915), CPABE (0.8916), AES (0.5277), Blowfish (0.6144), and ECC (0.4287), accordingly. Finally, this multi-level authentication using an improved QKD approach is analysed under various measures and validates the enhancement over the state-of-the-art models.

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利用改进的量子密钥分配实现云安全的多级认证。
云计算是一种通过互联网在线开发、配置和修改应用程序的按需虚拟技术。它使用户能够处理各种操作,如数据的存储、备份和恢复、数据分析、软件应用程序的交付、新服务和应用程序的实施、网站和博客的托管以及音频和视频文件的流式传输。因此,云计算为我们带来了许多好处,尽管由于数据泄露、数据丢失、网络攻击等与云计算安全相关的问题,云计算也受到了质疑。为了解决安全问题,研究人员开发了各种认证机制。这意味着建议方法中使用的认证程序是多层次的。因此,我们提供了一种更好的 QKD 方法,以加强云安全,抵御不同类型的安全风险。增强型 QKD 的密钥生成基于 ABE 公钥加密方法。这里,一种名为 CPABE 的方法被用于改进型 QKD。改进型 QKD 的 KCA 攻击评分为 0.3193,优于 CMMLA (0.7915)、CPABE (0.8916)、AES (0.5277)、Blowfish (0.6144) 和 ECC (0.4287)。最后,使用改进的 QKD 方法对这种多层次身份验证进行了各种分析,并验证了与最先进的模型相比所取得的进步。
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来源期刊
Network-Computation in Neural Systems
Network-Computation in Neural Systems 工程技术-工程:电子与电气
CiteScore
3.70
自引率
1.30%
发文量
22
审稿时长
>12 weeks
期刊介绍: Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.
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