The complexity of cyberattacks in Cyber-Physical Systems (CPSs) calls for a mechanism that can evaluate the operational behaviour and security without negatively affecting the operation of live systems. In this regard, Digital Twins (DTs) are revolutionizing the CPSs. DTs strengthen the security of CPSs throughout the product lifecycle, while assuming that the DT data is trusted, providing agility to predict and respond to real-time changes. However, existing DTs solutions in CPS are constrained with untrustworthy data dissemination among multiple stakeholders and timely course correction. Such limitations reinforce the significance of designing trustworthy distributed solutions with the ability to create actionable insights in real-time. To do so, we propose a framework that focuses on trusted and intelligent DT by integrating blockchain and Artificial Intelligence (AI). Following a hybrid approach, the proposed framework not only acquires process knowledge from the specifications of the CPS, but also relies on AI to learn security threats based on sensor data. Furthermore, we integrate blockchain to safeguard product lifecycle data. We discuss the applicability of the proposed framework for the automotive industry as a CPS use case. Finally, we identify the open challenges that impede the implementation of intelligence-driven architectures in CPSs.
翻译:网络-物理系统中的网络攻击的复杂性要求建立一种机制,既能评估操作行为和安全,又不会对实时系统的运作产生不利影响。在这方面,数字双双正在使CPS发生革命性革命。DT在整个产品生命周期内加强CPS的安全,同时假设DT数据是可靠的,为预测和应对实时变化提供了灵活性。但是,CPS现有的DT解决方案受到多种利益攸关方之间不可信的数据传播和及时纠正流程的制约。这些限制加强了设计可靠分布式解决方案的重要性,而这种解决方案能够实时产生可操作的洞察力。为此,我们提议了一个框架,通过整合块链和人工智能智能智能智能智能,侧重于信任和智能的DTT。采用混合方法,拟议的框架不仅从CPS的规格中获取过程知识,而且还依靠AI来了解基于传感器数据的安全威胁。此外,我们整合了块链来保护产品生命周期数据。我们讨论了拟议中的汽车业框架作为CPS公开结构使用的挑战,从而阻碍CPS的落实。最后,我们确定了CPS公开结构使用的挑战。