A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments
作者机构：Department of Computer SystemsUniversidad Politécnica de MadridMadrid28031Spain Department of Geospatial EngineeringUniversidad Politécnica de MadridMadrid28031Spain
出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)
年 卷 期：2024年第141卷第10期
页 面：631-654页
核心收录：
学科分类：0839[工学-网络空间安全] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：6G networks noise injection attacks Gaussian mixture model Bessel function traffic filter Volterra filter
摘 要：Future 6G communications are envisioned to enable a large catalogue of pioneering *** will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control loops critical for managing Industry 5.0 deployments,digital agriculture systems,and essential *** provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and *** full automation will enhance industrial efficiency significantly,it concurrently introduces new cyber risks and *** particular,unattended systems are highly susceptible to trust issues:malicious nodes and false information can be easily introduced into control ***,Denialof-Service attacks can be executed by inundating the network with valueless *** anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical *** based on an exhaustive data collection to detect anomalies are precise but extremely *** models,with their limited understanding of mobile networks,can achieve precision rates no higher than 75%.Therefore,more general and transversal protection mechanisms are needed to detect malicious behaviors *** paper introduces a probabilistic trust model and control algorithm designed to address this *** model determines the probability of any node to be *** channels are pruned for those nodes whose probability is below a given *** trust control algorithmcomprises three primary phases,which feed themodel with three different probabilities,which are weighted and ***,anomalous nodes are identified using Gaussian mixture models and clustering ***,traffic patterns are studied using digital Bessel functions and the functional scalar ***,the in
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