Methods for detecting malware using machine learning

Abstract

The rapid evolution of malware poses significant challenges to cybersecurity. Traditional signature-based detection methods are no longer sufficient due to the increasing sophistication and polymorphism of modern malware. Machine learning (ML) has emerged as a powerful tool for detecting malware by analyzing patterns, behaviors, and features that are difficult to capture using conventional techniques. This paper explores various machine learning methods used in malware detection, including supervised, unsupervised, and deep learning approaches. We discuss feature extraction techniques, dataset preparation, model training, and evaluation metrics. Additionally, we highlight the advantages and limitations of ML-based malware detection systems and provide insights into future research directions. The findings underscore the importance of integrating machine learning into cybersecurity frameworks to enhance detection accuracy and mitigate emerging threats.