RETRACTED ARTICLE: A clustering approach for attack detection and data transmission in vehicular ad-hoc networks.

IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Atul Barve, Pushpinder Singh Patheja
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引用次数: 0

Abstract

We, the Editors and Publisher of Network: Computation in Neural Systems, have retracted the following article:Barve, A., & Patheja, P. S. (2023). A clustering approach for attack detection and data transmission in vehicular ad-hoc networks. Network: Computation in Neural Systems, 1-26. https://doi.org/10.1080/0954898X.2023.2279973Since publication, significant concerns have been raised about the fact that this article has substantial overlaps with the following article:Barve, A. & Patheja, P. S. (2023). A clustering approach for attack detection and data transmission in vehicular ad-hoc networks. Ad Hoc & Sensor Wireless Networks, 58. 1-2, p. 127-149.DOI: 10.32908/ahswn.v58.10375Further investigations by the Publisher revealed that these overlaps are present in all sections of the article, including the figures and tables without appropriate acknowledgement. Upon query, the authors agree that the article is a duplicate submission. As this is a serious breach of our Editorial Policies, we are retracting the article from the journal. The corresponding author listed in this publication has been informed.We have been informed in our decision-making by our editorial policies and the COPE guidelines.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as 'Retracted'.

车辆自组织网络中攻击检测和数据传输的聚类方法。
车载自组织网络(vanet)在智能城市和智能交通系统的应用中发挥着越来越重要的作用。然而,VANET通信的可靠性和稳定性面临着巨大的障碍。Natura 2000 (N2k)是全球最大的保护区协调网络,因其以保护为中心的管理结构缺乏战略眼光而受到严重批评。本研究提出了一个三阶段策略来解决这些问题,旨在有效和可持续地管理N2K场地。该新方法采用dnn辅助典型相关分析(DNN-CCAS),包括簇形成、簇头选择和爆发识别,以增强VANET安全性。车辆聚类从一种改进的k -辅音方法开始,通过AKCEM聚类强调位置和速度。通过线性测量漫步方法选择簇头,然后使用DNN-CCAS将数据安全传输到云,如果簇头被认为是正常的。所提出的方法优于现有的技术,达到了令人印象深刻的91%的准确率。这一综合战略不仅解决了VANET的通信挑战,而且旨在通过将战略愿景纳入保护实践,彻底改变N2K遗址的管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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