A Comparative Study of Machine Learning and Deep Learning Algorithms for Malware Detection

Abstract

Malware detection is a critical component of cybersecurity, with artificial intelligence (AI) playing a pivotal role in addressing evolving threats. This study conducts a comparative analysis of machine learning (ML) and deep learning (DL) algorithms for malware detection, evaluating Logistic Regression, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN). Using a dataset of Portable Executable (PE) files, the study assesses performance based on accuracy, precision, recall, and F1-score. Results indicate that Random Forest achieves the highest accuracy at 99.42%, closely followed by XGBoost at 99.41%. These findings highlight the efficacy of ML algorithms for text-based malware detection and suggest directions for future research with diverse data formats.