Security Frame Towards 6G Networks for Isolating False Data Using Artificial Intelligence Algorithm

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Hariprasath Manoharan, Gayathri Devi P
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引用次数: 0

Abstract

This research aims to identify and explore the significance of security aspects in the transition to Sixth Generation (6G) networks. Due to the lack of security features and absence of a data discovery process in the current infrastructure, the proposed approach is being built to discover data under low latency conditions. Furthermore, data analysis is conducted using an artificial intelligence algorithm that incorporates distributed and various decision-making processes to monitor the outcomes. The analysis focuses on the impact of influential factors during data transmission units, employing normalizations to prevent unauthorized users from accessing such transmissions. In order to enhance the security of data in 6G networks, data pairing circumstances are assessed, resulting in increased delivery units at the receiver side and prevention of data drops. In order to conduct a thorough study, the parametric design is evaluated under four different scenarios. The performance analysis is then carried out using two case studies. The results show that the suggested method decreases data drops to 2%, whereas the previous methodology has a data drop rate of 10%.

利用人工智能算法隔离虚假数据的6G网络安全框架
本研究旨在识别和探索安全方面在向第六代(6G)网络过渡中的重要性。由于当前基础设施缺乏安全特性和数据发现过程,因此所提出的方法旨在在低延迟条件下发现数据。此外,数据分析使用人工智能算法进行,该算法结合了分布式和各种决策过程来监控结果。分析侧重于数据传输单元中影响因素的影响,采用规范化来防止未经授权的用户访问此类传输。为了增强6G网络中数据的安全性,对数据配对情况进行评估,从而增加接收端的传输单元,防止数据丢失。为了进行深入的研究,参数化设计在四种不同的情况下进行了评估。然后使用两个案例研究进行性能分析。结果表明,该方法将数据丢失率降低到2%,而之前的方法的数据丢失率为10%。
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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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