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This study compares and evaluates different methods for malware detection. It specifically examines the effectiveness of ensemble learning, which combines predictions from multiple models (such as Decision Trees (DT), Support Vector Machines (SVM), and Random Forest (RF)) using a voting approach, against a advanced deep learning algorithm, the Deep Neural Network (DNN). The evaluation encompasses a rigorous assessment of these models using the widely recognized CIC-MalMem-2022 dataset. To optimize the model performance, a strategic feature selection process is employed, reducing the number of input variables and consequently streamlining training time. This consideration is pivotal in achieving efficient and practical detection solutions. The study covers both binary and multi-class classification scenarios, adding a layer of complexity to the evaluation process. By addressing multiple classification types, the research accounts for diverse real-world applications and extends the understanding of model behavior across different scenarios. In the context of performance evaluation, various metrics are employed to gauge the models’ effectiveness, including accuracy, precision, recall, F1-Score, and ROC-AUC. These metrics provide a comprehensive overview of the models’ predictive capabilities. Notably, in two distinct scenarios, the ensemble learning approach demonstrated substantial promise by attaining commendable accuracy rates, outperforming the DNN model.

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Comparing Ensemble Learning and Deep Neural Networks for Malware Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Hajar Ouazza,
  • Fadoua Khennou,
  • Abderrahim Abdellaoui

摘要

This study compares and evaluates different methods for malware detection. It specifically examines the effectiveness of ensemble learning, which combines predictions from multiple models (such as Decision Trees (DT), Support Vector Machines (SVM), and Random Forest (RF)) using a voting approach, against a advanced deep learning algorithm, the Deep Neural Network (DNN). The evaluation encompasses a rigorous assessment of these models using the widely recognized CIC-MalMem-2022 dataset. To optimize the model performance, a strategic feature selection process is employed, reducing the number of input variables and consequently streamlining training time. This consideration is pivotal in achieving efficient and practical detection solutions. The study covers both binary and multi-class classification scenarios, adding a layer of complexity to the evaluation process. By addressing multiple classification types, the research accounts for diverse real-world applications and extends the understanding of model behavior across different scenarios. In the context of performance evaluation, various metrics are employed to gauge the models’ effectiveness, including accuracy, precision, recall, F1-Score, and ROC-AUC. These metrics provide a comprehensive overview of the models’ predictive capabilities. Notably, in two distinct scenarios, the ensemble learning approach demonstrated substantial promise by attaining commendable accuracy rates, outperforming the DNN model.