# Machine Learning Methods for Attack Detection in the Smart Grid

arXiv: https://arxiv.org/abs/1503.06468 · HTML: https://mlsanity.com/paper/1503.06468/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2015.

## Built on by (most cited first, 18 of 18)

- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks](https://mlsanity.com/paper/2104.11846/) (2021)
- [Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks](https://mlsanity.com/paper/2112.13166/) (2021)
- [False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey](https://mlsanity.com/paper/2111.14251/) (2021)
- [Multi-Source Data Fusion for Cyberattack Detection in Power Systems](https://mlsanity.com/paper/2101.06897/) (2021)
- [IoTDevID: A Behavior-Based Device Identification Method for the IoT](https://mlsanity.com/paper/2102.08866/) (2021)
- [A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities](https://mlsanity.com/paper/2407.07966/) (2024)
- [Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids](https://mlsanity.com/paper/2102.09057/) (2021)
- [Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2210.13547/) (2022)
- [Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids](https://mlsanity.com/paper/2306.10420/) (2023)
- [Adversarial Classification of the Attacks on Smart Grids Using Game Theory and Deep Learning](https://mlsanity.com/paper/2106.03209/) (2021)
- [PowerFDNet: Deep Learning-Based Stealthy False Data Injection Attack Detection for AC-model Transmission Systems](https://mlsanity.com/paper/2207.10805/) (2022)
- [Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems](https://mlsanity.com/paper/2507.22239/) (2025)
- [ICSML: Industrial Control Systems ML Framework for native inference using IEC 61131-3 code](https://mlsanity.com/paper/2202.10075/) (2022)
- [Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids](https://mlsanity.com/paper/2206.12527/) (2022)
- [Extracting Physical Causality from Measurements to Detect and Localize False Data Injection Attacks](https://mlsanity.com/paper/2310.10666/) (2023)
- [Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems](https://mlsanity.com/paper/2601.00873/) (2025)
- [Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids](https://mlsanity.com/paper/2606.20415/) (2026)

## Abstract (arXiv)

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semi-supervised) are employed with decision and feature level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than the attack detection algorithms which employ state vector estimation methods in the proposed attack detection framework.