A Comprehensive Study for Malware Detection through Machine Learning in Executable Files

Abstract

Two methods are frequently used to analyze malware and start specimens: static analysis and dynamic analysis. Following analysis, distinct characteristics are retrieved to distinguish malware from benign samples. The detection capacity of malware is contingent upon the effectiveness with which discriminative malware characteristics are retrieved through analysis methods. While conventional approaches and techniques were used inadvertently, machine learning algorithms are now utilized to classify malware, which can deal with the complexity and velocity of malware creation. However, even though a few research papers have been published, recent classifications of signature, behavioral and hybrid machine learning is not introduced well. Based on this demand, we provide a comprehensive analysis of malware detection using machine learning, as well as address the different difficulties associated with building the malware classifier. Finally, future work is addressed to build an effective malware detection system by addressing different malware detection problems.