Modern smart grid systems are heavily dependent on Information and Communication Technology, and this dependency makes them prone to cyberattacks. The occurrence of a cyberattack has increased in recent years resulting in substantial damage to power systems. For a reliable and stable operation, cyber protection, control, and detection techniques are becoming essential. Automated detection of cyberattacks with high accuracy is a challenge. To address this, we propose a two-layer hierarchical machine learning model having an accuracy of 95.44 % to improve the detection of cyberattacks. The first layer of the model is used to distinguish between the two modes of operation (normal state or cyberattack). The second layer is used to classify the state into different types of cyberattacks. The layered approach provides an opportunity for the model to focus its training on the targeted task of the layer, resulting in improvement in model accuracy. To validate the effectiveness of the proposed model, we compared its performance against other recent cyber attack detection models proposed in the literature.

中文翻译：

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用于智能电网系统中网络攻击检测的顺序监督机器学习方法

现代智能电网系统严重依赖信息和通信技术，这种依赖使它们容易受到网络攻击。近年来，网络攻击的发生率有所增加，从而对电力系统造成了重大损害。为了实现可靠和稳定的运行，网络保护、控制和检测技术变得必不可少。以高精度自动检测网络攻击是一项挑战。为了解决这个问题，我们提出了一个准确率为 95.44% 的两层分层机器学习模型，以提高对网络攻击的检测。该模型的第一层用于区分两种操作模式（正常状态或网络攻击）。第二层用于将状态分类为不同类型的网络攻击。分层方法为模型提供了将训练重点放在层的目标任务上的机会，从而提高了模型的准确性。为了验证所提出模型的有效性，我们将其性能与文献中提出的其他近期网络攻击检测模型进行了比较。