Building a Malware Detection System Based on a Machine Learning Method

Abstract

Malware attacks are dangerous and difficult to detect and prevent. Therefore, the task of detecting signs of malware and alerting it for users or the system is very necessary today. One of the most effective malware detection approaches is applying machine learning or deep learning to analyze its behavior. There have been many studies and recommendations to analyze malicious behavior then combined with some sorting or clustering methods to find their signs. In this paper, we will propose a method to use machine learning to detect malicious signs based on their unusual behavior. Accordingly, in our research, we will conduct malicious analysis using static and dynamic analysis methods to detect abnormal behaviors and combine them with a supervised classification algorithm to the conclusion on malware behavior.