Smart Grid Security: A Verified Deep Reinforcement Learning Framework to Counter Cyber-Physical Attacks

TL;DR

This paper introduces a verified deep reinforcement learning framework designed to detect and counter cyber-physical attacks on smart grids, enhancing grid security with real-time, formally verified protection strategies.

Contribution

The work presents a novel DRL-based approach with formal safety verification for smart grid protection, capable of real-time deployment on GPU systems to mitigate cyber-physical threats.

Findings

Successfully neutralizes cyber-physical attacks on smart grids

Achieves real-time protection sequence execution on CUDA-enabled GPUs

Establishes new protection rules for grid models against attacks

Abstract

The distributed nature of smart grids, combined with sophisticated sensors, control algorithms, and data collection facilities at Supervisory Control and Data Acquisition (SCADA) centers, makes them vulnerable to strategically crafted cyber-physical attacks. These malicious attacks can manipulate power demands using high-wattage Internet of Things (IoT) botnet devices, such as refrigerators and air conditioners, or introduce false values into transmission line power flow sensor readings. Consequently, grids experience blackouts and high power flow oscillations. Existing grid protection mechanisms, originally designed to tackle natural faults in transmission lines and generator outages, are ineffective against such intelligently crafted attacks. This is because grid operators overlook potential scenarios of cyber-physical attacks during their design phase. In this work, we propose a safe Deep Reinforcement Learning (DRL)-based framework for mitigating attacks on smart grids. The DRL agent effectively neutralizes cyber-physical attacks on grid surfaces by triggering appropriate sequences of existing protection schemes. The safety of the DRL agent is formally verified through a reachability analysis method. Additionally, our framework is designed for deployment on CUDA-enabled GPU systems, which enables faster execution of these protection sequences and their real-time validation. Our framework establishes a new set of protection rules for grid models, successfully thwarting existing cyber-physical attacks.