aspg-28-3150-1792155143 20261016125223 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2024 7 2 Al-Seyday.T. Qenawy Intelligent Systems and Machine Learning Lab, Shenzhen 518000, China Hussein Alkattan Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia Amany Khaled Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Mansoura University, Mansoura, Egypt This paper provides a detailed review of related works for classifying secure DNS traffic, with emphasis on the identification of threats relating to DoH using machine learning algorithms. In the present study, with the help of DoHBrw-2020 dataset consisting the network traffic data of DoH protocol during its testing phase, we compare the performance of various machine learning algorithms: Decision Tree, SVM, KNN, Na¨ıve Bayes, Neural Network (MLP), Gradient Boosting, and SVM with RBF kernel. As for each model, we have Accuracy, Sensitivity, Specificity, Positive Predicted Value, Negative Predicted Value, and F Score. They reveal the fact that the chosen Decision Tree model produces the highest accuracy and equals to 99. 65% and all the criteria of the assessment should be well managed. It is important that the various machine learning methods contribute to the study’s discovery of high potential in improving DNS traffic security and offers an understanding on the best models to use for real-time detection of DoH threats. From these outcomes, it can draw many perspectives to the further creation and implementation of safer DNS solutions within contemporary information security paradigms. 2024 73 81 3150 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.070207 https://www.americaspg.com/journal/28/article/3150 https://www.americaspg.com/storage/81724007244.pdf