# A Sequential Supervised Machine Learning Approach for Cyber Attack Detection in a Smart Grid System

arXiv: https://arxiv.org/abs/2108.00476 · HTML: https://mlsanity.com/paper/2108.00476/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2021.

## Built on by (most cited first, 4 of 4)

- [ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection](https://mlsanity.com/paper/2310.09298/) (2023)
- [LEMDA: A Novel Feature Engineering Method for Intrusion Detection in IoT Systems](https://mlsanity.com/paper/2404.16870/) (2024)
- [Unleashing the Power of Unlabeled Data: A Self-supervised Learning Framework for Cyber Attack Detection in Smart Grids](https://mlsanity.com/paper/2405.13965/) (2024)
- [Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids](https://mlsanity.com/paper/2510.02371/) (2025)

## Abstract (arXiv)

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.