基于多属性的移动边缘网络欺骗检测改进聚类算法

Shida Xia, N. Li, Tao Xiaofeng, Chao Fang
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引用次数: 2

摘要

基于信息中心网络(ICN)的移动边缘计算(MEC)网络近年来受到越来越多的关注。分布式网络架构带来了新的安全问题,尤其是身份安全问题。由于在MEC网络边缘部署了云平台,可以方便地获取和处理多个通道属性。为此,本文提出了一种基于多信道属性的欺骗检测机制。为了进一步降低复杂度,我们还提出了一种改进的聚类算法。仿真结果表明,所提出的欺骗检测方法能够以极低的复杂度提供接近最优的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiple Attributes Based Spoofing Detection Using an Improved Clustering Algorithm in Mobile Edge Network
Information centric network (ICN) based Mobile Edge Computing (MEC) network has drawn growing attentions in recent years. The distributed network architecture brings new security problems, especially the identity security problem. Because of the cloud platform deployed on the edge of the MEC network, multiple channel attributes can be easily obtained and processed. Thus this paper proposes a multiple channel attributes based spoofing detection mechanism. To further reduce the complexity, we also propose an improved clustering algorithm. The simulation results indicate that the proposed spoofing detection method can provide near-optimal performance with extremely low complexity.
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