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

Mehmet Necip Kurt, Oyetunji Ogundijo, Chong Li, Xiaodong Wang

2018-09-14Reinforcement Learning Cyber Attack Detection Outlier Detection Anomaly Detection reinforcement-learning

Abstract

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.

Related Papers

Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems2025-07-21 CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning2025-07-18 VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning2025-07-17 Spectral Bellman Method: Unifying Representation and Exploration in RL2025-07-17 Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback2025-07-17 VAR-MATH: Probing True Mathematical Reasoning in Large Language Models via Symbolic Multi-Instance Benchmarks2025-07-17 QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation2025-07-17 Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities2025-07-17