# IoT Botnet Attack Detection Model Based on DBO-Catboost
## 3 Methods
### IDBO-Catboost Model  
Optimizing the hyperparameters of a machine learning model can significantly impact its performance [34]. However, tuning hyperparameters can be time-consuming and computationally expensive. Hyperparameter tuning can be carried out through manual trial-and-error, or by using relatively simple algorithms such as random search, grid search, or Bayesian optimization. While manual tuning is straightforward, it often leads to local optima. On the other hand, random search and grid search can partially mitigate these issues, but are less efficient when dealing with high-dimensional parameter spaces. Bayesian optimization offers a more efficient approach for finding optimal hyperparameters, but it requires defining a probability model and may demand additional computational resources to train the model. In comparison to these methods, the dung beetle optimization algorithm is a novel swarm intelligence optimization algorithm that demonstrates fast convergence and strong global search capabilities. It can effectively discover the optimal combination of parameters in machine learning models. When predicting botnet attacks using the Catboost model, inappropriate parameter settings can have a significant impact on the model's predictions. Therefore, in this paper an improved dung beetle optimization (IDBO) algorithm was chosen to optimize the important hyperparameters of the Catboost model. The ensemble model solves the problem of theCatboost model easily, becoming stuck in local optima while further improving the accuracy of the predictions. The design concept of the IDBO-Catboost detection model combines the dung beetle optimizer algorithm with the Catboost algorithm, optimizing the key parameters in Catboost that have the most significant impact on classification performance. This approach leverages the parameter optimization capabilities of the DBO algorithm and combines it with the strengths of Catboost in handling classification problems, achieving an organic integration of the two. It eliminates the tedious manual tuning process. The IDBO-Catboost detection model is illustrated in Figure 1.  
The optimization process of Catboost using the improved dung beetle optimizer can be described in the following steps:  
Step 1: Initialize the Catboost model parameters and determine the optimal range based on experience. Set algorithm control parameters such as population size, maximum number of iterations, and position boundaries. Randomly define the positions of the beetles in the search space and form a new population according to the centroid opposition-based learning strategy. Step 2: Calculate the fitness values of all beetles based on the objective function. Step 3: Start the loop and update the positions of the beetles. Step 4: Check if each beetle is out of bounds. Step 5: Calculate the individual best and global best solutions, as well as their fitness values, based on the current positions of the beetles. The individual best represents the best solution found by each beetle, and the global best is selected from these individual best solutions. Figure 1: IDBO-Catboost Botnet Detection Model. Step 6: Repeat the above steps until the stopping criteria are met--either the maximum number of iterations is reached or the model performance reaches a pre-defined threshold. When the algorithm finishes, the global best solution and its fitness value are outputted. The global best solution represents the optimal parameter combination for the Catboost algorithm, and the fitness value represents the classification performance of the model in terms of AUC (Area Under the Curve). In this experiment, the parameters that have the most significant impact on the performance of the Catboost algorithm are the number of gradient boosting trees (iterations), learning_rate, and maximum tree depth (depth). The dung beetle optimizer is used to search for the optimal parameters for these parameters. The pseudocode for constructing the IDBO-Catboost model is shown in Algorithm 1.