A Sequential Supervised Machine Learning Approach for Cyber Attack Detection in a Smart Grid System
This addresses cyberattack detection for smart grid reliability, but it appears incremental as it builds on existing detection models with a layered approach.
The paper tackled the problem of automated cyberattack detection in smart grid systems by proposing a two-layer hierarchical machine learning model, achieving an accuracy of 95.44%.
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.