# DNS over HTTPS Tunneling Detection System Based on Selected Features via Ant Colony Optimization
## 3 The Proposed Work
### Algorithms in the Proposed Model  
(1) Ant Colony Optimization:  
ACO was developed by [PERSON] in 1992. It is a novel technique for solving combinatorial optimization problems [24]. Figure 3 shows the three levels of ant behavior in finding the shortest path between the nest and the food source. In the first level, ants explore randomly across multiple paths without any pheromone guidance, resulting in equal probabilities for all paths. In the second level, as ants reach the food source and return to the nest, they deposit pheromones along their routes, with shorter paths naturally receiving pheromones more frequently because of quicker traversal. By the third level, the shortest path becomes dominant as it accumulates the highest concentration of pheromones, attracting most ants, while longer or less efficient paths gradually lose their pheromone trails. This iterative process, driven by pheromone deposition and evaporation, ensures that a colony collectively optimizes its route to the food source. In feature selection, the process begins with initializing a population of artificial ants and a pheromone matrix that influences the selection of features. Each ant constructs a solution by probabilistically choosing features on the basis of the pheromone levels and heuristic information such as feature importance. These selected subsets of features are then evaluated via an objective function such as classification accuracy to measure their performance on the basis of updat  
Figure 2: Proposed methodology diagram. ing the evaluation pheromone [25]. Consequently, higher-quality solutions receive stronger pheromone deposits to reinforce successful feature combinations, whereas weaker solutions experience pheromone evaporation. The algorithm iterates through these steps until a termination condition is met, such as a set number of iterations or a convergence criterion. (2) XgBoost:  
XgBoost is a high-performance ML algorithm based on the gradient boosting framework for classification and regression problems. First developed in 2014 by [PERSON], XgBoost enhances both the performance and the efficiency of classic gradient boosting through various pertinent innovations, including regularization to reduce overfitting, efficient handling of sparse data and reading, and parallelized tree construction for faster training [26]. The algorithm sequentially builds an ensemble of decision trees, each of which attempts to minimize the mistakes made by the previous trees in the sequence by using gradient descent on a differentiable loss function [27]. Figure 3: ACO work mechanism for finding the shortest path. K Nearest Neighbors:  
The KNN algorithm is a simple yet powerful ML algorithm used for classification and regression. Proposed in the 1950s, KNN is a nonparametric, instance-based algorithm based on the principle of proximity to make predictions [26]. KNN For classification, once we have a given test instance on the basis of a distance metric, such as Euclidean distance, it identifies the 'k' closest data points (neighbors) in the feature space. The predicted class for the test instance is the majority class among these neighbors [2]. The most important of these include the fact that KNN is simple and easy to implement and is flexible since it can adapt to linear and nonlinear relationships in the data. However, this approach can be costly for large datasets, as it needs to compute the distance for each data point in the given dataset, and it is also sensitive to the selection of 'k' and the scaling of the input features [28]. Random Forrest:  
RF is an ensemble learning algorithm predominantly used for classification and regression and was developed by [PERSON] in 2001 [29]. It operates by constructing a multitude of decision trees during training and combining their outputs to improve accuracy and robustness. Each tree in the forest is built on a random subset of the data and a random subset of features, which helps to reduce variance and avoid overfitting, a common issue with individual decision trees.