## 2 Review of Literature
They found that XGBoost had the highest score with an accuracy rate of 0.97. In addition, [PERSON] et al. [15] proposed an integrated protection mechanism for networks of IoT devices using the UNSWNB15 dataset by combining blockchain algorithms and ML techniques. The model was proposed for threat detection and activation of secure information procedures. For the ML, K-Nearest Neighbor (KNN) was used for the classification. Several types of attacks such as fuzzers, DoS, backdoor etc. were covered in their study, with the best results for fuzzers attack with an accuracy rate of 0.99. Identifying their key characteristics is crucial to detecting malicious attacks. Therefore, [PERSON] et al. [16] developed a feature selection algorithm for filtering insignificant variables called Dynamic Feature Selector (DFS). Feature selection algorithms such as person correlation, XGB feature importance and information gain were used. The study used two datasets NSLKDD and UNSW-NB15. Several classifiers were compared and combined such as Bi-LSTM, RF, CNN-LSTM, Gated Recurrent Units (GNU) and DT. The bagging method was used to combine the base learner's decision. RF outperformed the other classifiers for NSLKDD and UNSW-NB15 datasets with an accuracy of 0.9965 (NSLKDD) and 0.9276 (UNSW-NB15). For NSLKDD dataset, the feature set was reduced to 50%. Many studies have recognized how destructive False Data Injection Attacks (FDIA) are for AC systems and create defensive mechanisms. One of them was developed by [PERSON] et al. [17] to build a new mechanism that detects any erroneous data injection in the AC-state estimation (SE) based on a signal processing technique. The proposed method is based on the analysis of successive states of the system by single wavelet entropy (SWE). The approach is used in different case studies to identify cyberattacks with different types of false data injection. The simulation results showed that the proposed FDIA detection method perform outstanding and achieving an accuracy rate of 0.965. Another study was conducted to prevent FDIA of AC systems reported by [PERSON] et al. [18] and propose a novel FDIA detection technique aimed at assessing the AC state. When malicious data is introduced into the state vectors, the temporal and spatial data correlations may differ from those found under normal operational settings. The proposed technique used wavelet transform and deep neural network methods for detection. Furthermore, the performance of the proposed approach was evaluated using extensive case studies of 300-bus and IEEE 118-bus power systems. The results outperformed conventional FDIA detectors and showed that the developed mechanism can detect attacks with greater than 0.90 accuracy. Furthermore, [PERSON] et al. [19] developed a DL-based classification scheme to detect and classify FDIA using the dataset consisting of 30,000 measurements. The proposed model used a conditional deep belief network (CDBN) integrating both conditional gaussian-bernoulli RBM (CGBRBM) with the conventional DB. Furthermore, two realistic adversary attacks were considered to test their model: the least-effort and multiple attacks. The study achieved an accuracy of 0.984 in verifying multiple scenarios and more than 90% for robustness and scalability verification. Ultimately, the proposed model surpassed the accuracy and robustness of both the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) for detecting FDIA. [PERSON] et al. [20] introduced a speech biometric framework for cyber-attack mitigation f to distinguish between synthetic and authentic human voices. They used the empirical mode decomposition (EMD) method in combination with the mel-frequency cepstral coefficient (MFCC) and SVM classifier. They used three different datasets for their experiments with, 960, 1152, and 12,624 sentences. Furthermore, their method outperformed other methods with an accuracy of over 0.90, which is crucial for biometric security. Inaddition, [PERSON] et al.