Virtual-real Fusion Simulation Technology and Application Research for Industrial Control Systems Cybersecurity of Process Manufacturing

doi:10.16182/j.issn1004731x.joss.24-1395

Abstract

Abstract:

Aiming at the problem that the industrial control system in the process manufacturing industry lacks an effective attack and defense drill platform when facing network attacks, it is difficult to truly simulate the attack situation, verify the protective measures, and accurately evaluate the impact of attacks on the physical system, an industrial control cybersecurity simulation technology based on virtual-real fusion is proposed to build an efficient attack and defense drill range. The industrial control cybersecurity simulation architecture based on virtual-real fusion is designed, and the consistency analysis of virtual-real fusion data is carried out. At the same time, in order to meet the requirements of cyber physical system in terms of synchronous data, state consistency, and response time, three key technologies of virtual-real fusion network construction, virtual-real scene mapping, and virtual-real data interaction are proposed. Taking TE (tennessee eastman) process as an example, a virtual-real fusion simulation platform for industrial control cybersecurity is built. The feasibility and effectiveness of the proposed technology are verified by simulation experiments, which provides an important technical support for the network security management of process manufacturing system.

Key words: process manufacturing, industrial control system, cyber security, virtual-real fusion, simulation platform

CLC Number:

TP391.9

Wang Xinwei, Wang Jinjiang, Wang Zheng, Zhang Laibin. Virtual-real Fusion Simulation Technology and Application Research for Industrial Control Systems Cybersecurity of Process Manufacturing[J]. Journal of System Simulation, 2026, 38(1): 84-98.

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References 18

[1]	高洋, 王礼伟, 任望, 等. 基于强化学习的工控系统恶意软件行为检测方法[J]. 工程科学学报, 2020, 42(4): 455-462.
	Gao Yang, Wang Liwei, Ren Wang, et al. Reinforcement Learning-based Detection Method for Malware Behavior in Industrial Control Systems[J]. Chinese Journal of Engineering, 2020, 42(4): 455-462.
[2]	杨挺, 许哲铭, 赵英杰, 等. 数字化新型电力系统攻击与防御方法研究综述[J]. 电力系统自动化, 2024, 48(6): 112-126.
	Yang Ting, Xu Zheming, Zhao Yingjie, et al. Review on Research of Attack and Defense Methods for Digitalized New Power System[J]. Automation of Electric Power Systems, 2024, 48(6): 112-126.
[3]	马标, 金映言, 那幸仪, 等. 工业控制系统入侵检测技术研究综述[J]. 计算机应用与软件, 2023, 40(5): 10-18, 43.
	Ma Biao, Jin Yingyan, Na Xingyi, et al. A Summary of Research on Industrial Control System Intrusion Detection Technology[J]. Computer Applications and Software, 2023, 40(5): 10-18, 43.
[4]	宗学军, 郭鑫, 何戡, 等. 面向工业控制网络的入侵检测方法研究[J]. 重庆理工大学学报(自然科学), 2023, 37(7): 208-216.
	Zong Xuejun, Guo Xin, He Kan, et al. Research on Intrusion Detection Methods for Industrial Control Network[J]. Journal of Chongqing University of Technology(Natural Science), 2023, 37(7): 208-216.
[5]	王泽鹏, 马超, 张壮壮, 等. 动态决策驱动的工控网络数据要素威胁检测方法[J]. 计算机研究与发展, 2024, 61(10): 2404-2416.
	Wang Zepeng, Ma Chao, Zhang Zhuangzhuang, et al. Dynamic Decision-driven Threat Detection Method for Data Elements in Industrial Control Networks[J]. Journal of Computer Research and Development, 2024, 61(10): 2404-2416.
[6]	陈泽, 邬桐, 左晓军, 等. 基于知识图谱的电力网络安全漏洞挖掘系统设计[J]. 制造业自动化, 2023, 45(7): 100-105, 110.
	Chen Ze, Wu Tong, Zuo Xiaojun, et al. Design of Power Network Security Vulnerability Mining System Based on Knowledge Graph[J]. Manufacturing Automation, 2023, 45(7): 100-105, 110.
[7]	李彤彤, 王诗蕊, 张耀方, 等. 面向工控系统漏洞的多维属性评估[J]. 计算机工程与科学, 2023, 45(2): 261-268.
	Li Tongtong, Wang Shirui, Zhang Yaofang, et al. Multi-dimensional Attribute Analysis of Industrial Control System Vulnerability[J]. Computer Engineering and Science, 2023, 45(2): 261-268.
[8]	宋晶, 刁润, 周杰, 等. 工业控制系统功能安全和信息安全策略优化方法[J]. 信息网络安全, 2022, 22(11): 68-76.
	Song Jing, Diao Run, Zhou Jie, et al. The Optimization Method of Industrial Control System Functional Safety and Information Security Policy[J]. Netinfo Security, 2022, 22(11): 68-76.
[9]	张琦. 虚假数据注入攻击下电力信息物理系统的安全防御研究[D]. 兰州: 兰州理工大学, 2022.
	Zhang Qi. Research on Security Defense of Power Cyber Physical System Under False Data Injection Attacks[D]. Lanzhou: Lanzhou University of Technology, 2022.
[10]	Craig P A, Mortensen J, Dagle J E. Metrics for the National SCADA Test Bed Program[EB/OL]. (2008-12-05) [2024-11-19]. .
[11]	Lee E J, Michalski J M. National SCADA Test Bed: FY05 Progress on Virtual Control System Environment(VCSE)[R]. [S.l. : s.n.], 2006.
[12]	Abo-khalil A G. Digital Twin Real-time Hybrid Simulation Platform for Power System Stability[J]. Case Studies in Thermal Engineering, 2023, 49: 103237.
[13]	Ayyalusamy V, Sivaneasan B, Kandasamy N K, et al. Hybrid Digital Twin Architecture for Power System Cyber Security Analysis[C]//2022 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia). Piscataway: IEEE, 2022: 270-274.
[14]	Pfister P, Wymore M L, Jacobson D, et al. Design and Implementation of a Cyber Physical Testbed for Security Training[C]//Proceedings of the 12th USENIX Conference on Cyber Security Experimentation and Test. USA: USENIX Association, 2019: 1.
[15]	Bi Jun. FINE: Future Internet iNnovation Environment[J]. China Communications, 2015, 12(1): 146-147.
[16]	孙健. 工控网络仿真靶场的虚拟化场景构建研究[D]. 哈尔滨: 哈尔滨工业大学, 2021.
	Sun Jian. Research on Virtual Scence Construction of Industrial Control Network Simulation Range[D]. Harbin: Harbin Institute of Technology, 2021.
[17]	邓博, 毛红宇, 王晓锋, 等. 云平台支撑下的虚实网络融合仿真方法[J]. 小型微型计算机系统, 2018, 39(3): 478-483.
	Deng Bo, Mao Hongyu, Wang Xiaofeng, et al. Emulation Method for the Fusion of Virtual and Physical Networks Supported by Cloud Platform[J]. Journal of Chinese Computer Systems, 2018, 39(3): 478-483.
[18]	蒋兵兵, 卜佑军, 王涵, 等. 基于数字孪生的内生安全网络靶场建模[J]. 网络安全技术与应用, 2023(3): 10-13.

场景	节点	延迟D/ms	一致性偏差C
正常场景	real_node	1.45	0.20
	sim_node[0]	2.75	3.06
	sim_node[1]	2.64	2.69
攻击场景	real_node	4.59	12.89
	sim_node[0]	2.85	3.42
	sim_node[1]	4.30	10.89

攻击场景	攻击对象	攻击类型	安全防护策略	攻击行为
1	主机	漏洞攻击	未配置	对主机漏洞进行弱口令爆破攻击，以获取主机控制权限
2	PLC	控制指令攻击	未配置	伪装成上位机向PLC发送恶意指令
3	PLC	控制指令攻击	已配置	伪装成上位机向PLC发送恶意指令

对比维度	虚实融合技术	全实物仿真技术	全虚拟仿真技术
成本与安全性	降低实验成本，避免对真实系统的破坏性影响	复杂场景模拟成本高，且可能导致真实设备受损	成本低，但缺乏实际物理设备参与，实验可信度较低
攻击模拟效果	能够真实还原网络攻击对物理系统的影响	能够直接观测攻击对实际设备的影响，但缺乏灵活调整能力	基于虚拟环境推理，缺乏物理系统的真实反馈
仿真灵活性与策略验证	支持多场景快速切换和动态调整，可验证真实场景下防护策略的可靠性	场景固定，难以动态调整与场景扩展，无法模拟动态复杂环境	可快速扩展，但无法涵盖真实设备的动态行为和复杂特性