False Data Injection Attack Detection based on Hilbert-Huang Transform in AC Smart Islands

Moslem Dehghani,Sanjeevikumar Padmanaban,Taher Niknam,Mohammad Ghiasi,Abdollah Kavousi-Fard

doi:10.1109/access.2020.3027782

Moslem Dehghani, Sanjeevikumar Padmanaban + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.3027782

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Abstract

In Smart Island (SI) systems, operators of power distribution system usually utilize actual-time measurement information as the Advanced Metering Infrastructure (AMI) to have an accurate, efficient, advanced control and monitor of whole their system. SI system can be vulnerable to complicated information integrity attacks such as False Data Injection Attack (FDIA) on some equipment including sensors and controllers, which can generate misleading operational decision in the system. Hence, lack of detailed research in the evaluation of power system that links the FDIAs with system stability is felt, and it will be important for both assessment of the effect of cyber-attack and taking preventive protection measures. In this regards, time-frequency-based differential approach is proposed for SI cyber-attack detection according to non-stationary signal assessment. In this paper, non-stationary signal processing approach of Hilbert-Huang Transform (HHT) is performed for the FDIA detection in several case studies. Since various critical case studies with a small FDIA in data where accurate and efficient detection can be a challenge, the simulation results confirm the efficiency of HHT approach and the proposed detection frame is compared with shallow model. In this research, the configuration of the SI test case is developed in the MATLAB software with several Distributed Generations (DGs). As a result, it is found that the HHT approach is completely efficient and reliable for FDIA detection target in AC-SI. The simulation results verify that the proposed model is able to achieve accuracy rate of 93.17% and can detect FDIAs less than 50 ms from cyber-attack starting in different kind of scenarios.

Highlights

Cyber Physical Systems (CPSs) usually concentrate on connecting the physical globe to the cyber and digital world; they are greatly utilized in the control of various industrial systems until several individuals can be able to grasp numerous kinds of required information in the real time [1]–[3]
MOTIVATION AND PRINCIPAL CONTRIBUTIONS OF THIS PAPER In this study, we propose an Hilbert–Huang Transform (HHT)-based approach to draw out the dominant ingredients of a signal according to those ingredients
It is important to figure out the AC-False Data Injection Attack (FDIA) attack mode, which can provide an opportunity for CPS of power systems to increase reliability and performance economics by developing appropriate interactions

Summary

INTRODUCTION

Cyber Physical Systems (CPSs) usually concentrate on connecting the physical globe to the cyber and digital world; they are greatly utilized in the control of various industrial systems until several individuals can be able to grasp numerous kinds of required information in the real time [1]–[3]. Reference [24] proposed a semantic system grid-based orientation detection to find attacks on control processing with utilizing grid traffic from water and sewage plants Such kinds of studies display the emphasis on research in CPS safety; in the SGs that include CPSs. The PMU or synchro-phasor was built upon the cyber layer to serve real-time information [25] which can act as a bridge among physical and cyber amplitudes [26]. Authors of the paper [27] presented a machine learning behavior-based method for the intrusion detection, and the data set that they utilized was Secure Water Treatment (SWaT)-generated information from eighteen attacks with ten type models. The signal spectral energy can be given in equation (9)

FDIAS THREATS

MAIN CONSTRUCTION OF AC-FDIAs

SMART ISLAND

Findings

DISCUSSION

CONCLUSION