Secure5G: A Deep Learning Framework Towards a Secure Network Slicing in 5G and Beyond
Secure5G: A Deep Learning Framework Towards a Secure Network Slicing in 5G and Beyond
复制标题
Secure5G：实现 5G 及后续安全网络切片的深度学习框架
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Poonam Kankariya
中科院分区：
文献类型：
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作者：
Anurag Thantharate;R. Paropkari;V. Walunj;C. Beard;Poonam Kankariya
网络切片将在支持大量5G应用、用例和服务方面发挥至关重要的作用。网络切片功能将在切片之间提供端到端隔离，并能够根据服务需求(带宽、覆盖范围、安全、延迟、可靠性等)定制每个切片。在切片之间保持资源、流量和网络功能的隔离对于保护网络基础架构系统免受分布式拒绝服务(DDoS)攻击至关重要。5G网络需求和支持不断增长和复杂的业务需求的新功能集使现有的网络安全方法变得力不从心。在本文中，我们开发了一个基于神经网络的安全5G网络切片模型，在5G核心网络入侵之前，根据传入连接主动检测和消除威胁。安全5G是一种弹性模型，它隔离从设备(S)到核心网络以及任何外部网络的威胁，确保端到端的安全。我们设计的模型将使网络运营商能够将网络切片作为服务进行销售，以便在单个基础设施上高效地提供高安全性和可靠性的各种服务。
Network Slicing will play a vital role in enabling a multitude of 5G applications, use cases, and services. Network slicing functions will provide an end-to-end isolation between slices with an ability to customize each slice based on the service demands (bandwidth, coverage, security, latency, reliability, etc.). Maintaining isolation of resources, traffic flow, and network functions between the slices is critical in protecting the network infrastructure system from Distributed Denial of Service (DDoS) attack. The 5G network demands and new feature sets to support ever-growing and complex business requirements have made existing approaches to network security inadequate. In this paper, we have developed a Neural Network based ‘Secure5G’ Network Slicing model to proactively detect and eliminate threats based on incoming connections before they infest the 5G core network. ‘Secure5G’ is a resilient model that quarantines the threats ensuring end-to-end security from device(s) to the core network, and to any of the external networks. Our designed model will enable the network operators to sell network slicing as-a-service to serve diverse services efficiently over a single infrastructure with high security and reliability.