A Novel False Data Injection Attack Detection Model of the Cyber-Physical Power System

Jie Cao,Da Wang,Pengcheng Xu,Kewei Hu,Kai Xue,Zhaoyang Qu,Mingshi Cui

doi:10.1109/access.2020.2995772

Abstract

The False data injection attack (FDIA) against the Cyber-Physical Power System (CPPS) is a kind of data integrity attack. With more and more cyber vulnerabilities detected out, different types of FDIAs are emerging as severe threats to the stable operation of CPPS gradually. In this paper, the invasion pathway of the FDIA against CPPS is explored in detail, and a novel FDIA detection model based on ensemble learning is further provided. First, a pseudo-sample database is built to assist the training and evaluation of this model, and it's more important to update the model in the future. Furthermore, the optimal feature set is extracted to characterize the behavior of the FDIA, which improves the precision of the FDIA detection model. Finally, a focal-loss-lightgbm (FLGB) ensemble classifier is constructed to detect the FDIA behavior automatically and accurately. We illustrated the performance of this model by a fusion of measurement data and power system audit logs. This model utilizes the offline training way, the conclusion shows the high precision and stability of this model, which ensures the stable operation of the smart grid and improves the FDIA resistance ability of the CPPS.

Highlights

The Cyber-Physical Power System (CPPS) fuses computing equipments, communication systems, and the physical power grid into a multidimensional, isomerous, and complex system [1], [2]
EXPERIMENTAL ANALYSIS we evaluate the performance of our proposed CKS-FCS-FLGB model for False data injection attack (FDIA) detection
The default values, the meanings, and the effects of the parameters of FLGB are as shown in Table 4: The total accuracy, average recall, average precision, and average F1-Score are used as performance indicators to evaluate the FDIA detection model

Summary

INTRODUCTION

The CPPS fuses computing equipments, communication systems, and the physical power grid into a multidimensional, isomerous, and complex system [1], [2]. More kinds of FDIA have emerged, the attacker can use network vulnerabilities to tamper with the measurement data, control data, even the equipment information with the minimum cost, which cause the large-scale chain failures [8]. If attackers get a sufficient understanding of the information and protection algorithms of the power grid, they can build false data that evades the state estimation algorithm. The detection methods based on time series prediction mainly include the statistical consistency detection, the sequential consistency detection, and sensor trajectory prediction [15]–[17] This kind of method predicts the distribution of state variables based on the operating law of the system state and the historical database. The power system can’t respond in time when FDIA happens, so it is not suitable for the FDIA detection of complex systems

THE DETECTION METHOD BASED ON MACHINE LEARNING

THE DETECTION MODEL OF NEW EXTENSION OF FDIA

EXPERIMENTAL ANALYSIS

DATASETS

Findings

CONCLUSION