Digital twin is a virtual copy of their “real-world counterparts”, which refer to physical objects, systems, or entities that exist in the physical world. They share valuable information about how the system works by connecting the replica with the real product via the internet and sensors. In digital twin organizations, industrial control systems and the internet are the main sources of data collection and synchronization, resulting in an increase in cyber-attacks. As a result, they could cause data leaks and be used by hackers to launch attacks without being detected. Researching the appropriate defenses against such attacks is of paramount interest. To that end, in this study, we build an integrated model utilizing the combination of a Dynamic Bayesian Network (DBN) and a Discrete-Time Markov Chain (DTMC) to analyze the considerable consequences of cyberattacks on a digital twin-based organization over different time period. DTMC models recovery and vulnerability for DT-based organizations, and then it is paired with the DBN model to simulate the cyberattack propagation behavior in the organization's IT performance. Next, we calculate the organization's and under two different cyber-attack scenarios to determine its IT performance and financial behavior. Finally, we evaluate the organization's IT system's reliability and resilience after implementing two prevention mechanisms. Result indicates that Distributed Denial-of-Service (DDoS) cyberattack can cause more destruction to the digital twin organization's IT system than a malware attack in terms of cyberattack propagation, work and utility level, reliability, and resilience capability. The findings of the analysis hold practical applicability in real-world settings, enabling the identification of high-risk cyberattacks within DT-based organizations, the analysis and prediction of an organization's IT performance during actual cyberattacks, and the formulation of effective prevention strategies to address cyber security concerns within DT-based organizations.

中文翻译：

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分析网络攻击对基于数字孪生的工业组织绩效的影响


数字孪生是“现实世界对应物”的虚拟副本，“现实世界对应物”指的是物理世界中存在的物理对象、系统或实体。他们通过互联网和传感器将复制品与真实产品连接起来，分享有关系统如何工作的宝贵信息。在数字孪生组织中，工业控制系统和互联网是数据收集和同步的主要来源，导致网络攻击增加。因此，它们可能会导致数据泄露，并被黑客用来发起攻击而不被发现。研究针对此类攻击的适当防御措施至关重要。为此，在本研究中，我们利用动态贝叶斯网络（DBN）和离散时间马尔可夫链（DTMC）的组合构建了一个集成模型，以分析网络攻击对基于数字孪生的组织在不同网络上的严重后果。时间段。 DTMC对基于DT的组织的恢复和脆弱性进行建模，然后与DBN模型配对来模拟组织IT性能中的网络攻击传播行为。接下来，我们计算组织在两种不同网络攻击场景下的 和 ，以确定其 IT 性能和财务行为。最后，我们在实施两种预防机制后评估组织 IT 系统的可靠性和弹性。结果表明，在网络攻击传播、工作和效用水平、可靠性和恢复能力方面，分布式拒绝服务（DDoS）网络攻击比恶意软件攻击对数字孪生组织的 IT 系统造成更大的破坏。 分析结果在现实环境中具有实际适用性，能够识别基于DT的组织内的高风险网络攻击，分析和预测组织在实际网络攻击中的IT性能，并制定有效的预防策略来应对基于 DT 的组织内的网络安全问题。