Research On Novel Security Defense Technologies For Beyond 5G Intelligent Networking

Beyond 5G(B5G)will be a new generation mobile communication system that uses artificial intelligence(AI)as the core,uses higher frequency bands as signal carriers,and has data rates up to terabits per second.B5 G has become the commanding height of scientific and technological competition among main countries all over the world due to its characteristics of ultra-high speed,large throughput,strong robustness and other significant potential impacts on various industries.Existing researches have discussed some basic theories of B5 G on antenna designing,terahertz signal processing,and polarization code encoding.However,with the continuous expansion of the application space,the research focus of mobile communication systems is changing from cloud center to the network edge.In the future,the performance of B5 G will be largely constrained by the networking mode and its security protection capabilities.B5G is a ubiquitous information fusion network.Its intelligent networking architecture will be compatible with Software-Defined Networking(SDN),Information-Centric Networking(ICN),Mobile Edge Computing(MEC),etc.and need to support seamless switching between different networking modes.However,from the cloud to the edge,the distribution of computing and storage resources in the network is uneven,this makes the security protection technology of B5 G intelligent networking more complicated and worse controllable.Moreover,new threats brought by artificial intelligence,such as dataset leakage,model defects,and sample poisoning,make it impossible for traditional security protection technologies to be applied in B5 G.Therefore,this article deeply studies the novel security protection technology for B5 G intelligent networking,enriching the basic theories of B5 G cloud network traffic management,cloud-edge integration resource scheduling and computing task assignment,attack mitigation and secure knowledge sharing of edge computing,adversarial examples defence of edge learning,We propose the SDN/ICN traffic application-layer awareness and fine-grained QoS optimization,service popularity-based dynamic scheduling of heterogeneous resources,knowledge popularity-based complex task adaptation,and consensus information randomly-encrypted based collusion attack mitigation,Adversarial sample attack recognition and Decentralized Vigilance defense.The innovative research work of this paper can be summarized as follows:First,Application Layer Awareness and Fine-grained QoS Optimization Under Multi-tenant Software-Defined Environment.In view of the problems of virtually-sliced link,software-defined function,and multi-tenant resource usage under B5 G intelligent networking,the deep packet inspection-based application-layer behavior awareness method for SDN,application-layer SLA awareness mechanism for multi-tenant SDN and application-layer Qo E awareness for massive ICN traffic were established,and ultimately an active bandwidth allocation technology for multi-tenant SDN was realized.Those approaches optimize QoS/Qo E performance(Including bandwidth utilization,jitter,delay,etc.)under the SDN/ICN networking environment.Second,Dynamic Scheduling of Heterogeneous Resources and Secure Knowledge Sharing in MEC environment.In view of the problems of untrusted nodes and unbalanced resource distribution in mobile edge computing,study the dynamic scheduling method of heterogeneous resources based on service popularity,improve the adaptability of complex computing tasks like deep learning to the MEC environment;study the knowledge-centric network model,propose the basic framework of ”edge learning as a service”,establish a collusion attack mitigation and secure knowledge sharing mechanism in an untrusted environment,reduce the service response delay of B5 G intelligent networking.Third,Adversarial Examples Recognition and Multi-Stage Immune Defense Under Edge Learning Environment.This article focuses on the detection and defense methods of adversarial samples.It proposes: 1)decentralized adversarial samples recognition and fast vigilance mechanism;2)decentralized adversarial examples Multi-stage immune defence method.Unlike only enhancing the robustness and generalization ability of a single deep learning model,the proposed mechanism can identify adversarial samples from input data,correct errors made by neural networks,and send early-warnings to the other nodes.For the deep learning model deployed at the network edge,the proposed multi-stage immune defense method treats dynamically reduce the impacts of adversarial examples by sharing,orchestrating and consensusing the information of gradient parameters and network structure of multiple edge neural network models.In summary,contrary to cloud network,cloud-edge integration,edge computing,and edge learning four different scenarios,this paper studies and proposes novel security protection methods for B5 G intelligent networking under three different environments.And also,this paper tested the correctness,effectiveness,and superiority of the proposed methods on the mainstream experimental platforms using public dataset.This paper provides theoretical support and important reference for the further development of B5G.