Advanced sensing is a key ingredient for intelligent control in Industrial Internet of Things (IIoT) environments. Coupled with enhanced communication capabilities, sensors are becoming increasingly vulnerable to cyberattacks, thereby jeopardizing the often safety-critical underlying cyber-physical system. One prominent approach to sensor-level attack detection in modern industrial environments, named pasad, has recently been proposed in the literature. Pasad is a process-aware stealthy-attack detection mechanism that has shown promising capabilities in detecting anomalous, potentially malicious behavior through real-time monitoring of sensor measurements. Although fast and lightweight, a major limitation of pasad is that it is univariate, meaning that only a single sensor can be monitored by one instance of the algorithm. This impediment poses serious concerns on its scalability, especially in modernized industrial environments, which typically employ a plethora of sensors. This paper generalizes pasad to the multivariate case, where a plurality of sensors can be monitored concurrently with little added complexity. This generalization has the evident advantage of offering scalability potential for deployment in future-focused industrial environments, which are undergoing growing integration between the digital and physical worlds.

高级感测是工业物联网（IIoT）环境中智能控制的关键要素。加上增强的通信功能，传感器变得越来越容易受到网络攻击，从而危及了通常对安全至关重要的基础网络物理系统。最近在文献中提出了一种在现代工业环境中用于传感器级攻击检测的杰出方法，称为pasad。Pasad是一种可感知过程的隐身攻击检测机制，通过对传感器测量值的实时监控，该机制已显示出在检测异常（潜在的恶意行为）方面很有前途的功能。尽管快速且轻巧，但是pasad的主要局限性它是单变量的，这意味着该算法的一个实例只能监视单个传感器。这种障碍严重影响了其可扩展性，特别是在现代化的工业环境中，这种环境通常使用过多的传感器。本文概括pasad多变量的情况下，其中多个传感器可以被具有很少增加的复杂性的同时进行监测。这种概括具有明显的优势，即为在面向未来的工业环境中部署提供了可扩展性的潜力，而工业环境正经历着数字世界与物理世界之间日益增长的集成。