Ecevit, Mert İlhanColhak, FurkanDağ, HasanEcevit, Mert IlhanDag, HasanCreutzburg, Reiner2024-10-152024-10-1520240979835034960397983503495972996-5322https://doi.org/10.1109/COINS61597.2024.10622643This study highlights the effectiveness of deep neural network (DNN) models, particularly those integrating natural language processing (NLP) and multilayer perceptron (MLP) techniques, in detecting malicious domain registrations compared to traditional machine learning (ML) approaches. The integrated DNN models significantly outperform traditional ML models. Notably, DNN models that incorporate both textual and numeric features demonstrate enhanced detection capabilities. The utilized Canine + MLP model achieves 85.81% accuracy and an 86.46% F1-score on the MTLP Dataset. While traditional ML models offer advantages such as faster training times and smaller model sizes, their performance generally falls short compared to DNN models. This study underscores the trade-offs between computational efficiency and detection accuracy, suggesting that their superior performance often justifies the added costs despite higher resource requirements.eninfo:eu-repo/semantics/closedAccessDomain Name System (DNS)CybersecurityMachine LearningDeep Neural Network (DNN)Natural Language Processing (NLP)Malicious Domain DetectionConference Object8285WOS:00129888030001610.1109/COINS61597.2024.10622643N/AN/A