CLASSIFICATION ALGORITHMS FOR ANOMALY DETECTION TECHNIQUES

 CLASSIFICATION ALGORITHMS

 

Decision Trees:

Decision trees are data classification systems. Decision trees are a collection of rules that categorize data based on attribute values. Decision trees are a popular categorization and prediction technique. A decision tree is a tree that is made up of three basic parts: nodes, edges, and leaves. A decision tree may be used to categorize a data point by beginning at the root of the tree and working your way up until you reach a leaf node. The categorization of the data point would then be provided by the leaf node. A tree may be built in two ways: top-down and bottom-up. ID3 and C4.5 are two popular decision tree algorithms that employ a top-down strategy. Before it can be used to categorize new data, the decision tree must first be trained with existing data.

There are three sorts of nodes in a decision tree: decision nodes, chance nodes, and end nodes. Decision nodes represent attributes-based options, chance nodes indicate non-end node predictions following a decision, and end nodes reflect the actual results. 

Support Vector Machines (SVM):

SVM is a supervised machine learning technique that may be used for classification as well as regression analysis. The fundamental learning process consists of mapping the original input space to a higher dimensional (n-dimensional) feature space and then concluding a hyperplane inside the feature space with a minimal margin using Sequential Minimum Optimization (SMO) or other approaches.

When compared to other algorithms, Support Vector Machine (SVM) is the most reliable classification technique in machine learning since it offers rapid and simple prediction procedures. It classifies data points in a data repository based on support vectors to create a hyperplane that separates the class labels into their associated classes. Because of the hyperplane, the whole data will classify with respective parameters with the algorithm procedure that will operate the variables and deviate the network malicious attack accordingly.

The training dataset is first moved to a higher dimensional feature space, and the linear non-separable issue is then converted into a linearly dividable problem using a kernel function. In anomaly detection difficulties, all normal instances are classified in one class, while various harmful instances are placed in another. Following that, the network observations are routed via a hyperplane with the largest margins at the nearest points in each sector. Only patterns that are extremely near to the margin have an effect on how these margins are computed. The remaining patterns might be deleted without affecting the outcome.

Bayesian Networks:

A Bayesian network, also known as a belief network, is a probabilistic graphical model that depicts a collection of random variables and their conditional relationships as a directed acyclic graph. Each node represents a random variable, while the edges indicate relationships. The unconnected nodes reflect variables that are conditionally independent of one another. Before it can classify unknown data, the Bayesian Network learns the informal relationships between characteristics and class labels from the training dataset.

K-Nearest Neighbor:

The k-nearest neighbor's algorithm (KNN) is a non-parametric classification and regression approach. The outcome of k-NN classification is a class membership. A majority vote of its neighbors classifies an item, with the object allocated to the class most prevalent among its k nearest neighbors (k is a positive integer, typically small). If k=1, the object is simply assigned to the class of the item's single nearest neighbor. The object's property value is the outcome of k-NN regression. This value is the average of its k nearest neighbors' values. Using NIDS, the classification nodes are connected to the neighbors to examine the attack variables and it classifies the nodes which are different from the rest of the nodes

 K-Means Algorithm:

One of the most well-known centroid algorithms is the k-means method. It divides the dataset into k subgroups so that all points in each subset are around the same center. It chooses k instances at random to represent cluster centers, and the remaining instances are allocated to their nearest cluster center depending on the selected instances. K-means then computes the new cluster centers by averaging all data points in the same cluster. The method is repeated until some condition of convergence is reached. Datasets which is processed for NIDS will simultaneously classify the node which will cluster similar points and it will evaluate the group of then subgroups to predict the sequence of layer where the malicious process presents.

Random Forest (RF).

It generates a certain number of trees, with a random selection mechanism used to choose which variables to include in each model. It uses bagging and random feature selection for tree construction. Because the tree is formed completely without trimming, each tree has a smaller bias and the correlation of each tree has a low variation. The approach of combining the produced trees produces acceptable prediction accuracy while minimizing over-fitting. In RF, there are various trimmable learning criteria, such as variable selection in each node, which is normally kept constant for all nodes, tree size, and forest size. In comparison to other ensemble learners, RF has various advantages, including reduced processing work because each tree is formed on a limited number of variables and simpler implementation in a parallel computing technique, which can further speed up the algorithm.