September 19, 2021

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A Sequential Supervised Machine Learning Approach for Cyber Attack Detection in a Smart Grid System. (arXiv:2108.00476v1 [cs.CR])

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.