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

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. INDEX TERMS CPPS, FDIA detection model, invasion pathway analysis, ensemble learning.

Machine Learning Algorithm for Detection of False Data Injection Attack in Power System

“…In recent times, ML is used for FDIA detection in parallel with earlier state estimation and time-series analysis methods [11]. The performance of ML-based techniques has improved over time, and some interesting research is carried out in this domain.…”

“…The authors have used a publicly available dataset that contains readings of four PMUs and communication meta-data from IDS and firewall [7]. Cao et al [11] discussed the scope and performance of ensemble learning for FDIA detection in great detail by explaining FDIA in the context of Cyber-Physical Power System (CPPS). The power system dataset [7] was used to train and test four ML algorithms (One R, J-Ripper, Random Forest (RF), and Naive Fig.…”

VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

“…LightGBM has a maximum depth parameter, it expands like a tree but prevents overfitting. Gradient boosting, due to its tree structure, is known to be good for tabular data but recently researchers have found it useful in a various applications [55][56][57][58][59][60][61][62][63][64][65][66][67].…”

State Estimation within IED Based Smart Grid Using Kalman Estimates

“…Since the smart grid is an Internet of Things (IoT) based network where every section of the network is interlinked, hence, cyber-attacks to a certain part of the network could take control of the complete grid [2]. The attackers could use it for further malicious motives such as shutdown, cascaded blackouts, or terrorism activities [3]. Therefore, the protection of smart grid from FDIAs and cyber-attacks require immediate attention.…”