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Towards Fully Secure 5G Ultra-Low Latency Communications: A Cost-Security Functions Analysis

Computers, Materials & Continua 2023年第1期74卷 855-880页

作者： borja bordel Ramón Alcarria Joaquin Chung Rajkumar Kettimuthu Tomás Robles Iván Armuelles Universidad Politecnica de Madrid MadridEspana Argonne National Laboratory LemontILUSA Universidad de Panama PanamaPanama

Future components to enhance the basic,native security of 5G networks are either complex mechanisms whose impact in the requiring 5G communications are not considered,or lightweight solutions adapted to ultrareliable low-latency communications(URLLC)but whose security properties remain under discussion.Although different 5G network slices may have different requirements,in general,both visions seem to fall short at provisioning secure URLLC in the future.In this work we address this challenge,by introducing cost-security functions as a method to evaluate the performance and adequacy of most developed and employed non-native enhanced security mechanisms in 5G networks.We categorize those new security components into different groups according to their purpose and deployment scope.We propose to analyze them in the context of existing 5G architectures using two different approaches.First,using model checking techniques,we will evaluate the probability of an attacker to be successful against each security solution.Second,using analytical models,we will analyze the impact of these security mechanisms in terms of delay,throughput consumption,and reliability.Finally,we will combine both approaches using stochastic cost-security functions and the PRISM model checker to create a global picture.Our results are first evidence of how a 5G network that covers and strengthened all security areas through enhanced,dedicated non-native mechanisms could only guarantee secure URLLC with a probability of∼55%.

关键词： 5G networks security analysis secure low latency communications URLLC eMBBC

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Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems

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Computers, Materials & Continua 2023年第9期76卷 3189-3218页

作者： borja bordel Sánchez Ramón Alcarria Tomás Robles Information Technologies Department Universidad Politécnica de MadridMadrid28031Spain Geospatial Information Department Universidad Politécnica de MadridMadrid28031Spain

Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a new non-linear generalized model to describe Cyber-Physical Systems.This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and randomeffects in the physical and computationalworlds.Besides,the digitalization stage in hardware devices is represented too.Attackers and most critical sparse sensor attacks are described through a stochastic process.The reconstruction and protectionmechanisms are based on aweighted stochasticmodel.Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics(such as the Fourier transform,first-return maps,or the probability density function).A decision algorithm calculates the final reconstructed value considering the previous error probability.An experimental validation based on simulation tools and real deployments is also carried out.Both,the new technology performance and scalability are studied.Results prove that the proposed solution protects Cyber-Physical Systems against up to 92%of attacks and perturbations,with a computational delay below 2.5 s.The proposed model shows a linear complexity,as recursive or iterative structures are not employed,just algebraic and probabilistic functions.In conclusion,the new model and reconstructionmechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks,even in dense or pervasive deployments and scenarios.

关键词： Cyber-physical systems sparse sensor attack non-linear models stochastic models security

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