Comprehensive Framework for Malware Detection: Leveraging Ensemble Methods, Feature Selection and Hyperparameter Optimization

Malware detection is a critical task in today's digital landscape, where the proliferation of malicious attacks poses significant threats to data security and privacy. In this paper, we propose a comprehensive framework for malware detection that encompasses various stages, including ensemble model, cross-validation, feature selection methods, and hyperparameter tuning. Through extensive experimentation and evaluation, we explore different combinations and techniques to achieve optimal results. Our approach incorporates ensemble methods, such as LightGBM, along with other base classifiers. Feature selection techniques, including PCA, tree-based, and correlation with the target variable technique, are applied to identify informative features for improved detection accuracy. Additionally, we examined diverse hyperparameter tuning methods, such as GridSearch, Random Search, and Bayesian Search, to optimize model performance. The results demonstrate the effectiveness of our framework in detecting malware and highlight the potential for practical application in real-world scenarios.