False data injection attack in smart grid: Attack model and reinforcement learning-based detection method

Abstract

The smart grid, as a cyber-physical system, is vulnerable to attacks due to the diversified and open environment. The false data injection attack (FDIA) can threaten the grid security by constructing and injecting the falsified attack vector to bypass the system detection. Due to the diversity of attacks, it is impractical to detect FDIAs by fixed methods. This paper proposed a false data injection attack model and countering detection methods based on deep reinforcement learning (DRL). First, we studied an attack model under the assumption of unlimited attack resources and information of complete topology. Different types of FDIAs are also enumerated. Then, we formulated the attack detection problem as a Markov decision process (MDP). A deep reinforcement learning-based method is proposed to detect FDIAs with a combined dynamic-static detection mechanism. To address the sparse reward problem, experiences with discrepant rewards are stored in different replay buffers to achieve efficiency. Moreover, the state space is extended by considering the most recent states to improve the perception capability. Simulations were performed on IEEE 9,14,30, and 57-bus systems, proving the validation of attack model and efficiency of detection method. Results proved efficacy of the detection method in different scenarios.

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

“…Moreover, the authors of [29], [30], [31] explained the potential of ML techniques to detect various attacks on CPS, including smart grids, power grids, and cyber-physical power systems. Lin et al [29] used deep reinforcement learning (DRL), propose a model for false data injection attacks and counter-detection techniques. Jahangir et al [30] proposed a novel approach for the identification and localization of highresolution.…”

“…This study is limited by its use of both the KDD99 and NSL-KDD datasets, which do not adequately represent recent attacks and suffer from network biases. [29] DRL The study's contributions encompass a model for conducting false data injection attacks, a detection method based on deep reinforcement learning, and an approach to enhance efficiency in addressing sparse reward problems.…”

Security Analysis for Smart Healthcare Systems

“…It is used to train an IDS AI-based model to detect certain attacks that are recorded and tabulated in the dataset. The type of attack is mainly Man-In-The-Middle (MITM) [ 54 ], which consists of spoofing [ 55 ] and data injection attacks [ 56 ]. It contains flow network metrics in addition to patient biometrics.…”