Abstract

With the latest advances in computer and networking technologies, the threat of cyber-physical attacks against manufacturing systems is growing. Unlike traditional cyber-attacks, cyber-physical attacks are not limited to intellectual property theft and affect the physical world, which could be devastating to manufacturing, if they are undetected. Relying on traditional quality control to defend against these malicious attacks, manufacturers can choose to either closely monitor a large number of potential quality characteristics or only monitor a specific subset of the characteristics. However, the former choice may be impractical when a large number of potential characteristics exists, whereas the latter might be susceptible to an intelligently designed attack that targets unmonitored characteristics. Therefore, a novel random variable-selection approach that is both resilient to malicious cyber-physical attacks and sensitive to shifts over a small subset of characteristics is proposed in this work. Such an approach is based upon random sampling strategies when using multivariate Hotelling T² control charts. To assess its usefulness, the proposed approach was compared to an established variable-selection method, using a simplified cost model. The obtained results show that the proposed approach is both cost-effective and well-suited for industrial applications where the number of quality characteristics to monitor is quite significant.

摘要

随着计算机和网络技术的最新进展，对制造系统的网络物理攻击的威胁越来越大。与传统的网络攻击不同，网络物理攻击不仅限于知识产权盗窃，还会影响物理世界，如果未被发现，这可能对制造业造成毁灭性打击。依靠传统的质量控制来抵御这些恶意攻击，制造商可以选择密切监控大量潜在的质量特征，或者仅监控特征的特定子集。然而，当存在大量潜在特征时，前一种选择可能不切实际，而后一种选择可能容易受到针对未受监控特征的智能设计攻击的影响。所以，在这项工作中提出了一种新颖的随机变量选择方法，它既能抵御恶意网络物理攻击，又能对一小部分特征的变化敏感。这种方法基于使用多元 Hotelling 时的随机抽样策略T ²控制图。为了评估其有效性，使用简化的成本模型将所提出的方法与已建立的变量选择方法进行了比较。获得的结果表明，所提出的方法既具有成本效益，又非常适合需要监控的质量特性数量非常重要的工业应用。