# Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/1809.05258 · HTML: https://mlsanity.com/paper/1809.05258/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2018.

## Built on by (most cited first, 15 of 15)

- [Reinforcement Learning for IoT Security: A Comprehensive Survey](https://mlsanity.com/paper/2102.07247/) (2021)
- [Reinforcement Learning for Feedback-Enabled Cyber Resilience](https://mlsanity.com/paper/2107.00783/) (2021)
- [Intrusion Prevention through Optimal Stopping](https://mlsanity.com/paper/2111.00289/) (2021)
- [Learning Near-Optimal Intrusion Responses Against Dynamic Attackers](https://mlsanity.com/paper/2301.06085/) (2023)
- [Learning Intrusion Prevention Policies through Optimal Stopping](https://mlsanity.com/paper/2106.07160/) (2021)
- [Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations](https://mlsanity.com/paper/2202.11917/) (2022)
- [Learning Security Strategies through Game Play and Optimal Stopping](https://mlsanity.com/paper/2205.14694/) (2022)
- [Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization](https://mlsanity.com/paper/2508.14385/) (2025)
- [A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities](https://mlsanity.com/paper/2407.07966/) (2024)
- [Learning Optimal Defender Strategies for CAGE-2 using a POMDP Model](https://mlsanity.com/paper/2509.06539/) (2025)
- [Principled Data-Driven Decision Support for Cyber-Forensic Investigations](https://mlsanity.com/paper/2211.13345/) (2022)
- [Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid](https://mlsanity.com/paper/2306.07993/) (2023)
- [Reinforcement Learning Design for Quickest Change Detection](https://mlsanity.com/paper/2403.14109/) (2024)
- [Optimization Algorithms in Smart Grids: A Systematic Literature Review](https://mlsanity.com/paper/2301.07512/) (2023)
- [Deep Reinforcement Learning for Online Error Detection in Cyber-Physical Systems](https://mlsanity.com/paper/2302.01567/) (2023)

## Abstract (arXiv)

Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid. In the literature, outlier detection schemes making sample-by-sample decisions and online detection schemes requiring perfect attack models have been proposed. In this paper, we formulate the online attack/anomaly detection problem as a partially observable Markov decision process (POMDP) problem and propose a universal robust online detection algorithm using the framework of model-free reinforcement learning (RL) for POMDPs. Numerical studies illustrate the effectiveness of the proposed RL-based algorithm in timely and accurate detection of cyber-attacks targeting the smart grid.