Learning-Assisted Secure End-to-End Network Slicing for Cyber-Physical Systems
Learning-Assisted Secure End-to-End Network Slicing for Cyber-Physical Systems
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DOI：
10.1109/mnet.011.1900303
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发表时间：
2019-10
期刊：
影响因子：
9.3
通讯作者：
Qiang Liu;T. Han;N. Ansari
中科院分区：
文献类型：
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作者：
Qiang Liu;T. Han;N. Ansari
为了高效管理和安全运行，迫切需要将电网和车辆等物理系统互连起来。由于物理系统的特性千差万别，很难找到一种适合开发网络物理系统的通用网络解决方案。网络切片是一项很有前途的技术，它允许网络运营商在共享网络基础设施之上创建多个虚拟网络。这些虚拟网络可以定制，以满足不同的网络物理系统的需求。然而，设计安全的网络切片解决方案以有效地为各种网络物理系统创建端到端网络切片是一项挑战。在本文中，我们讨论了网络切片的挑战和安全问题，研究了学习辅助网络切片解决方案，并分析了它们在拒绝服务攻击下的性能。我们还提出了一个小型测试平台的设计和实现，用于评估网络切片解决方案。
There is a pressing need to interconnect physical systems such as power grid and vehicles for efficient management and safe operations. Due to the diverse features of physical systems, there is hardly a one-size-fits-all networking solution for developing cyber-physical systems. Network slicing is a promising technology that allows network operators to create multiple virtual networks on top of a shared network infrastructure. These virtual networks can be tailored to meet the requirements of different cyber-physical systems. However, it is challenging to design secure network slicing solutions that can efficiently create end-to-end network slices for diverse cyber-physical systems. In this article, we discuss the challenges and security issues of network slicing, study learning-assisted network slicing solutions, and analyze their performance under the denial-of-service attack. We also present a design and implementation of a small-scale testbed for evaluating the network slicing solutions.