Critical infrastructure, including refineries, pipelines and power grids are routinely monitored by supervisory control and data acquisition (SCADA) systems. The information exchange and communication aspects of such systems and their connected networks make them prone to cyberattacks. Providing SCADA systems with robust security and rapid cyber-attack detection is therefore imperative. Automatic intrusion detection can be provided by some machine learning methods, in particular, classification algorithms. However, such algorithms commonly disregard the difference between various misclassification errors. The techniques of cost-sensitive learning and Fisher's (linear) discriminant analysis (FDA) are separately investigated to overcome class imbalance issues in SCADA system datasets using five different machine learning algorithms applied to a well-studied gas pipeline dataset. The results reveal that the cost-sensitive learning is able to increase the performance of all the algorithms evaluated, especially their true positive rate. On the other hand, the FDA method can favorably influence only the HoeffdingTree and OneR algorithms. This suggests that the FDA method is not as powerful as the cost-sensitive learning in addressing class imbalance issues.

关键的基础设施，包括炼油厂，管道和电网，均由监督控制和数据采集（SCADA）系统进行例行监视。这种系统及其连接的网络的信息交换和通信方面，使它们易于遭受网络攻击。因此，必须为SCADA系统提供可靠的安全性和快速的网络攻击检测。可以通过某些机器学习方法（尤其是分类算法）提供自动入侵检测。但是，这种算法通常会忽略各种错误分类错误之间的差异。成本敏感型学习技术和Fisher's技术 使用五个不同的机器学习算法（已深入研究天然气管道数据集）对S（线性）判别分析（FDA）进行了单独研究，以克服SCADA系统数据集中的类不平衡问题。结果表明，成本敏感型学习能够提高所有评估算法的性能，尤其是其真实阳性率。另一方面，FDA方法只能有利地影响HoeffdingTree和OneR算法。这表明FDA方法在解决班级失衡问题方面不如对成本敏感的学习方法强大。FDA方法只能有利地影响HoeffdingTree和OneR算法。这表明FDA方法在解决班级失衡问题方面不如对成本敏感的学习方法强大。FDA方法只能有利地影响HoeffdingTree和OneR算法。这表明FDA方法在解决班级失衡问题方面不如对成本敏感的学习方法强大。