# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 4 False Data Injection Attacks Detection Modelling and Methods
### Detection Methods of FDIA
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detection Methods** & **References** & **Year** & **Datasets** \\ \hline MGD Based & [16] & 2017 & Synthetic Datasets in Matpower \\ KPCA & [17] & 2020 & Synthetic Datasets in Matpower \\ FFNN & [111] & 2020 & Random Data simulated in Matpower \\ RF, Adaboost & [128] & 2019 & Synthetic Datasets in Matpower \\ CDBN & [131] & 2017 & Synthetic Datasets in Matpower \\ Perceptron, k-NN, SLR & [137] & 2016 & Synthetic Datasets in Matpower \\ XGBoost & [138] & 2019 & Provided by Endsea \\ KF, DKF, EKF & [141; 142; 143; 144] & 2017, 2018 & Simulated \\ DSVM & [145] & 2017 & Synthetic Datasets in Matpower \\ MSA & [146] & 2017 & Synthetic Datasets in Simulink \\ UIO & [147] & 2019 & Random Data for each grid subarea \\ OCSVM & [155] & 2018 & Synthetic Datasets in Matpower \\ DT and SVM & [156] & 2016 & Real Dataset in USA \\ SVM Based & [157] & 2016 & Smart Energy Datasets from Ireland \\ SSVM Based & [158] & 2019 & Irish Smart Energy Trial Data \\ ANN & [159] & 2013 & Real Datasets in Brazil \\ PARX & [160] & 2016 & Synthetic Datasets in Matpower \\ ARIMA and ANN & [161] & 2015 & Real Datasets in Amsterdam \\ GBTD & [162] & 2019 & Irish Smart Energy Trial Data \\ RNN & [163] & 2018 & Synthetic Datasets in Matpower \\ CNN and Encryption & [164] & 2019 & Released by SGCC \\ MFEFD & [165] & 2019 & Irish Smart Energy Trial Data \\ KLD Based & [166] & 2015 & Synthetic Datasets in Matpower \\ SARSA & [167] & 2018 & Synthetic Datasets in Matpower \\ ISOF & [168] & 2019 & Synthetic Datasets in Matpower \\ Deep autoencoder & [169] & 2019 & Real PMU data \\ GAN & [170] & 2019 & IoT-based smart home data \\ RNN and CNN & [171] & 2019 & Released by SGCC \\ MLR and NN & [172] & 2019 & CEFcon 2012 \\ NNS and Game Theory & [173] & 2019 & Synthetic Datasets in Matpower \\ NB & [174] & 2019 & ISO New England \\ POMDP & [167] & 2018 & Synthetic Datasets in Matpower \\ LR and DBSCAN & [175] & 2018 & Real PMU Data \\ DRE & [176] & 2016 & Synthetic Datasets in Matpower \\ SVM \& ANN & [177] & 2019 & Nigerian Power Grid \\ C-Vine Copulas Based & [178] & 2016 & Low carbon London load dataset \\ DNN and LRC & [179] & 2019 & Released by SGCC \\ GoDec & [180] & 2011 & Simulated \\ ALM-based, LMaFit, GoDec & [181] & 2018 & Simulated \\ D-FACTS & [182] & 2012 & Synthetic Datasets in Matpower \\ D-FACTS & [183] & 2014 & Random Data simulated in Matpower \\ NARX & [184] & 2019 & Synthetic Datasets in Matpower \\ RPCA & [185] & 2011 & Released by SGCC \\ LMP & [186] & 2011 & Real Time Marketing Data \\ Subspace Methods & [187] & 2015 & Simulated Probability Detections \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of FDIA detection methods in the SGs. approach by comparing its performance with SVM and MLP; and four-phase classification is proposed in [16]:  
* Dimension degradation using PCA;
* Mixed Gaussian model structure using a positively labelled set;
* Collection of classification thresholds using a mixed dataset;
* An unlabelled dataset was used for testing. Another detection mechanism using SARSA(\(\lambda\)) is proposed in [167]: reinforcement learning algorithm with formulated problem of stealthy FDIA detection as a POMDP. In FDIA detection as matrix separation problem, it is difficult to get global optimum [180]. To address that, four algorithms are proposed in [181], the traditional ALM, double-noise-dual-problem ALM (DNDP-ALM), the LMaFit, and \"Bilateral random projections (BRP) with Go Decomposition (GoDec)\". GoDec achieves higher efficiency than others. Another new detection approach using D-FACTS (Distributed Flexible AC Transmission System) is analysed in [182; 183]. ANN-based State estimation method NARX in [184] and Robust Principal Component Analysis (RPCA) are examined in [185]. LMP method for FDIA detection is used in [186]. Different subspace methods and examined [187] Bayesian or another dynamic state approaches might be more appropriate to detection FDIA.