School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
Sensors (Basel). 2022 Mar 28;22(7):2593. doi: 10.3390/s22072593.
Industrial control systems (ICS) are applied in many fields. Due to the development of cloud computing, artificial intelligence, and big data analysis inducing more cyberattacks, ICS always suffers from the risks. If the risks occur during system operations, corporate capital is endangered. It is crucial to assess the security of ICS dynamically. This paper proposes a dynamic assessment framework for industrial control system security (DAF-ICSS) based on machine learning and takes an industrial robot system as an example. The framework conducts security assessment from qualitative and quantitative perspectives, combining three assessment phases: static identification, dynamic monitoring, and security assessment. During the evaluation, we propose a weighted Hidden Markov Model (W-HMM) to dynamically establish the system's security model with the algorithm of Baum-Welch. To verify the effectiveness of DAF-ICSS, we have compared it with two assessment methods to assess industrial robot security. The comparison result shows that the proposed DAF-ICSS can provide a more accurate assessment. The assessment reflects the system's security state in a timely and intuitive manner. In addition, it can be used to analyze the security impact caused by the unknown types of ICS attacks since it infers the security state based on the explicit state of the system.
工业控制系统（ICS）应用于许多领域。由于云计算、人工智能和大数据分析的发展导致更多的网络攻击，ICS 始终面临风险。如果在系统运行过程中发生风险，企业资本将面临危险。动态评估 ICS 的安全性至关重要。本文提出了一种基于机器学习的工业控制系统安全动态评估框架（DAF-ICSS），并以工业机器人系统为例。该框架从定性和定量两个角度进行安全评估，结合三个评估阶段：静态识别、动态监控和安全评估。在评估过程中，我们提出了一种加权隐马尔可夫模型（W-HMM），并使用 Baum-Welch 算法动态建立系统的安全模型。为了验证 DAF-ICSS 的有效性，我们将其与两种评估方法进行了比较，以评估工业机器人的安全性。比较结果表明，所提出的 DAF-ICSS 可以提供更准确的评估。该评估及时直观地反映了系统的安全状态。此外，由于它根据系统的明确状态推断安全状态，因此可以用于分析未知类型的 ICS 攻击造成的安全影响。
