A Novel Hybrid Feature Selection And Ensemble-based Machine Learning Approach For Botnet Detection

This article presents a proposed botnet detection approach that integrates hybrid feature selection strategies with an additional trees ensemble classifier. The model is able to take advantage of the combined intelligence of numerous decision trees and outperforms single-classifier-based techniques. The model exhibits robustness across diverse publically available datasets, and its capacity for generalization and adaptability to new, undiscovered botnet kinds is highlighted by its ability to perform well on various datasets. The proposed model’s increased performance metrics make it a very effective and strong tool for combating botnet threats across a variety of domains.