Reserach on Digital Twins Technology in Cyberspace Security

doi:10.16182/j.issn1004731x.joss.23-0853

Abstract

Abstract:

Combined with digital twins and cyber-space modeling and simulation, the network digital twins (NDT) technology with in-deep research can enable the development of diverse techniques of cyber security. The basic concept and research history of NDT are summarized, and a taxonomy is proposed to survey applications of NDT. A cyber security-oriented network digital twin model (CyS-NDT) is concluded through the literature. The relationship between the internal security problem of NDT and the method of enabling network security technology is discussed to prospect further challenges and opportunities.

Key words: digital twins, cyberspace modeling and simulation, cyberspace security, cyber defense, cyber evaluation

CLC Number:

TP393

Ren Qiankun, Xiong Xinli, Liu Jingju, Yao Qian. Reserach on Digital Twins Technology in Cyberspace Security[J]. Journal of System Simulation, 2024, 36(8): 1944-1957.

Figures/Tables 5

Fig. 1

Table 1

Summary of existing NDT architectures

技术实现方法	相关文献	主要研究	优点	缺点与不足
云计算	[9-14]	数据所有者从物理资产中生成数据并将其发送到云服务器，在虚拟空间中模拟数字孪生，将模拟结果与所有者共享，用户访问数据可以不受时间和地点的约束	提供按需服务、计算资源、无处不在的网络接入，拥有高性能的计算和存储服务	很难找到一种安全的方式来共享数据、存在隐私泄露问题
边缘计算	[15-23]	将数字孪生与边缘计算结合，建立一个数字孪生授权的边缘网络模型，解决了自适应边缘关联问题	减少了系统延迟，提高用户的实用性，解决了终端用户和服务器之间的不可靠、远距离通信的问题	边缘网络中接入点多且分散，边缘终端的防护能力不足，数据量大、网络不稳定等
网络切片	[24-27]	数字孪生技术通过创建支持切片的网络虚拟表示，以数字方式模拟其行为并预测时变性能，可以捕获不同切片之间的关系，并监控不同网络切片的端到端实时数据	对网络的安全状态进行分析和预测，对安全策略进行模拟、验证和优化，能够预测一些未知的安全问题	配置切片安全功能涉及到对大量网络功能和资源的管理和编排，动态管理的成本和复杂性相对较高，难以找到最佳的网络威胁解决方案
区块链	[28-31]	基于区块链的数据安全共享架构应用于数字孪生物联网系统，解决了物理系统、数字孪生系统和物联网应用系统之间的数据安全传输问题	拥有一个安全且保护用户隐私的身份验证协议，允许用户验证数据的安全性	无法抵御离线密码攻击、匿名属性攻击、临时信息攻击等各种攻击行为
知识图谱	[32-34]	提出了一种基于知识图谱构建的数字孪生网络，用知识图谱描述复杂的数字孪生网络，不仅可以表示网络的物理属性，而且可以从不同的维度挖掘网络关系	增强了数字孪生内部链接和引用、知识补全、错误检测、集体推理和语义查询能力，可以基于历史数据和推理结果使用知识图谱进行决策	孪生数据之间的关系难以确立，仅仅通过知识图谱很难做到数据的全面映射，且对于数据之间的关系没有判定过程
云边协同	[35-37]	基于数字孪生的云边缘协同架构描述了数字孪生驱动下实时可视化监控和云边缘协同控制的关键技术，验证了基于数字孪生的云边协同系统的有效性	解决了设备层与云端直接通信时传输实时性差、带宽不足、安全性低的问题	仅仅考虑了单一设备使用，没有利用多个设备同时构建孪生模型，效率较低

Table 1

Fig. 2

Fig. 3

Fig. 4

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