## 5 Methodology
Randomly selected data and characteristics train each decision tree to minimize overfitting and maximize generalization. Because  
Figure 3: The steps involved in the overall process. random forest is a machine learning method that can handle a wide range of data and tasks, it is frequently used for classification and regression problems [24; 25]. Aggregating predictions from individual trees can help obtain the prediction of the random forest as follows: \[t=\text{mode (predictions from individual trees)}\] (7)
* **SVM:** Support Vector Machine (SVM) is a machine learning approach for anomaly identification with only the majority class of data used for training. It finds a hyperplane encapsulating the majority class and recognizes anomalies as data points beyond this boundary during testing. It is useful for fraud detection and quality control when there is a goal of finding rare events in a regular dataset [22; 26]. The decision function of the One-Class SVM is used to determine if a data point is an anomaly: \[g(a)=sign(distance(a)-th)\] (8) where _distance(a)_ represents the distance of the datapoint, a, from the decision boundary, _which is a threshold_, or a predefined value. * **Decision Tree:** A decision tree is a machine learning technique that models predictions and choices as trees. It is used for classification and regression. Root nodes reflect the initial decisions or features in the tree. Leaf nodes indicate outcomes or predictions, whereas internal nodes reflect intermediate judgments. Decision trees divide data by impurity reduction or knowledge gain. They can be pruned to avoid overfitting. In classification, leaf nodes are class labels; in regression, they are numbers [27; 28]. The decision tree's prediction is made by traversing down the tree based on the feature values of the input sample until a leaf node is reached: _y = prediction at the leaf node_. * **Autoencoder:** The autoencoder is an artificial neural network variant that is commonly employed in unsupervised machine learning. The primary applications of this technique include dimensionality reduction, feature learning, and data compression. Autoencoders are composed of two main components: an encoder and a decoder. The primary objective of autoencoders is to reconstruct the input data, thereby acquiring a compressed representation of the data through unsupervised learning [29; 30]. The autoencoder learns to reconstruct its input data. The reconstruction error is used as a measure of an anomaly. The reconstruction error is calculated as follows: \[RSE(c)=\frac{1}{m}\sum_{i=1}^{m}(ci-c)^{2}\] (9) where \(ci\) represents the input data, \(c\) represents the reconstructed data, _and \(m\) represents several_ features. An anomaly is detected if the reconstruction error is above a predefined threshold. * **Logistic Regression:** Logistic regression is a supervised machine learning algorithm that represents the relationship between one or more independent variables (features) and the likelihood of a specific result occurring. It is often used to solve binary classification problems, such as when determining whether an email is spam, whether a customer would purchase a product, etc. [31]. The logistic regression model predicts the probability of the positive class using the logistic function as follows: \[Q(a=1|C)=\frac{1}{1+e^{-C\omega}}\] (10) where \(C\) represents the input features, and \(\omega\) indicates the model parameters. * **FFNN:** A feed-forward neural network (FFNN) is an artificial neural network in which information flows in a single direction: from the input layer to one or more hidden layers and then to the output layer. In the experiment, we used three layers: The first layer is the input layer, which receives input data. Each node in this layer representsone of the input's features. There is also a hidden layer, which is an intermediate layer between the input and output layers, where processing is carried out. Each node in a hidden layer is linked to every node in the preceding and subsequent layers.