# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 4 False Data Injection Attacks Detection Modelling and Methods
### Detection Methods of FDIA  
The detection part of cyber-security in SG is vital for resisting cyberattacks in its large-volume data-driven architecture. In this architecture, cybersecurity has become more complicated than before, and traditional manual and signature-based approaches are no longer useful have revealed the need for a new approach [99]. Thus, it has been heavily investigated in contemporary literature. The signature-based detection approaches for FDIAs have not prepared for data challenges caused by the large-scale deployment of PMU in the CPS on SG. Real-time big data produced by PMU causes storage and computational issues [133; 134; 135]. However, there is a remarkable fact that this issue becomes an opportunity for data analytics techniques such as Machine Learning (ML) to detect and block FDIAs. ML has excellent non-linear analysis capabilities to detect FDIA in more complex systems when more data is obtained from the system; it can be solving the challenges easier [133]. Thus, it is applied very beneficially in the smart grid's cybersecurity areas with complex sensor networks. For detecting FDIA, general ML techniques artificial neural network (ANN) and support vector machines (SVM) were the most recent works and used previously, while implementation of other techniques in such detection was also conducted. Different methods for detecting and identifying FDIA on SG have been proposed to in this section. Classify of different FDIA detection methods are depicted in Table 3. The attack to data integrity is a significant threat to energy consumption and the state estimation process. It becomes possible for attackers to make control centres and wrong decisions through manipulations of various SG measurements [136]. Remarkable methods are used for data integrity aim are particle swarm optimization (PSO), Bayesian framework (BF), Adaboost, Random Forests (RF), and Common Path Mining Method [128; 137; 138; 139; 140]. In [119], data analytical approaches are analysed, and the Margin-setting algorithm (MSA) is a novel data analytical approach applied to the system based on ML and MSA; it reaches better results than the ANN and SWM methods. It is the first work to use MSA to detect FDIAs. Kalman Filter (KF) is one of the primary detection methods for power state estimation process on online operation [141]. In [142], KF was utilized to detect FDIA in automatic generation control (AGC) systems. Extended Kalman filter (EKF) proposed in [143], and Distributed Kalman Filter (DKF) can be found in [144]. The benefit of EKF provides to reach more precise estimate and detection of FDIA. According to [145] distributed support vector machine (DSVM) algorithm is studied for training and principal component analysis (PCA) on an IEEE 118-bus system simulated by MATPOWER. In [146], a detection method on phasor measurement unit (PMU) using MSA for spherical classification with data from an IEEE 6 bus system simulated in MATLAB. The result is that FDIAs are stealth attacks that can overcome the existing detection scheme [146]. In [137] the methods used were perceptron, kernels nearest neighbours (k-NN), SVM with Gaussian and linear kernels, sparse logistic regression (SLR), and the semi-supervised SVM (S3 VM) studied on the models with using IEEE 9, 57, and 118 bus systems. In [131] conditional deep belief network (CDBN) which have one of the various deep neural network infrastructures, so catching is proposed to the high-dimensional temporal characteristics of the stealthy FDI attacks. Unknown input observation (UIO) was used in FDIA detection in [147]. The method which used supervised learning to classify measurement data is proposed in [137], and it was capable of identifying unobservable attacks and predict attacks using observation sets. Euclidean distance-based approach is proposed in [148] to detect FDIAs. They have also investigated on feature selection schemes with less complexity with improved accuracy that studied genetic algorithm for BDD.