# Smart Grid Cyber Attacks Detection using Supervised Learning and Heuristic Feature Selection

arXiv: https://arxiv.org/abs/1907.03313 · HTML: https://mlsanity.com/paper/1907.03313/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons](https://mlsanity.com/paper/2111.04881/) (2021)
- [A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities](https://mlsanity.com/paper/2407.07966/) (2024)
- [A Comprehensive Survey on the Cyber-Security of Smart Grids: Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions](https://mlsanity.com/paper/2207.07738/) (2022)
- [PowerFDNet: Deep Learning-Based Stealthy False Data Injection Attack Detection for AC-model Transmission Systems](https://mlsanity.com/paper/2207.10805/) (2022)
- [Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid](https://mlsanity.com/paper/2306.07993/) (2023)
- [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)
- [Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems](https://mlsanity.com/paper/2508.10035/) (2025)
- [Anomaly Detection with Ensemble of Encoder and Decoder](https://mlsanity.com/paper/2303.06431/) (2023)
- [A Deep Multi-Modal Cyber-Attack Detection in Industrial Control Systems](https://mlsanity.com/paper/2304.01440/) (2023)
- [CRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid](https://mlsanity.com/paper/2503.00358/) (2025)
- [Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids](https://mlsanity.com/paper/2510.02371/) (2025)

## Abstract (arXiv)

False Data Injection (FDI) attacks are a common form of Cyber-attack targetting smart grids. Detection of stealthy FDI attacks is impossible by the current bad data detection systems. Machine learning is one of the alternative methods proposed to detect FDI attacks. This paper analyzes three various supervised learning techniques, each to be used with three different feature selection (FS) techniques. These methods are tested on the IEEE 14-bus, 57-bus, and 118-bus systems for evaluation of versatility. Accuracy of the classification is used as the main evaluation method for each detection technique. Simulation study clarify the supervised learning combined with heuristic FS methods result in an improved performance of the classification algorithms for FDI attack detection.