Machine Learning in Malware Analysis: Current Trends and Future Directions

Abstract

Malware analysis is a critical component of cybersecurity due to the increasing sophistication and the widespread of malicious software. Machine learning is highly significant in malware analysis because it can process huge amounts of data, identify complex patterns, and adjust to changing threats. This paper provides a comprehensive overview of existing work related to Machine Learning (ML) methods used to analyze malware along with a description of each trend. The results of the survey demonstrate the effectiveness and importance of three trends, which are: deep learning, transfer learning, and XML techniques in the context of malware analysis. These approaches improve accuracy, interpretability, and transparency in detecting and analyzing malware. Moreover, the related challenges and Issues are presented. After identifying these challenges, we highlight future directions and potential areas that require more attention and improvement, such as distributed computing and parallelization techniques which can reduce training time and memory requirements for large datasets. Also, further investigation is needed to develop image resizing techniques to be used during the visual representation of malware to minimize information loss while maintaining consistent image sizes. These areas can contribute to the enhancement of machine learning-based malware analysis.