Emerging Machine Learning Techniques in Malware Detection and Analysis: A Comparative Analysis

Abstract

New types of malware with unique characteristics are being created daily in legion. This exponential increase in malwareis creating a threat to the internet. From the past decade, various techniques of malware analysis and malware detection have been developed to prevent the efficacy of malware. However, due to the fast-growing numbers and complexities in malware, it is getting difficult to detect and analyze the malware manually. Because of the inefficiency in manual malware analysis, automated malware detection and analysis would be a better solution. Thus, malware analysis supported by machine learning became a required part of malware analysis. The automation used in learning patterns in malware can help in efficiently identifying the complexities. Malware Analysis with help the Machine learning would be more efficacious in terms of automation and memory usage. In this paper, we conducted a review of emerging various ML (Machine Learning) strategies used so far, in the field of malware analysis, to give a comprehensive view of the existing processes. We systemized them on various aspects like their objectives, machine learning algorithms used, information about the malware, etc. We also highlighted the existing problems in this particular field of study and tried to find multiple ways in which advancements can happen concerning the current trends being used.