Data Science Approach for Malware Detection

Abstract

Large associations have a great deal of data moving all through their network. The data can begin from inward computer systems, IT infrastructures, and security mechanisms. In any case, these endpoints don’t speak with one another. The security innovation liable for detecting malware can’t generally observe the general image of attacks. The battle between security experts and malware designers is an endless fight with the intricacy of malware changing as fast as development develops. Present status of-the-workmanship research centers around the turn of events and utilization of machine learning (ML) methods for this malware detection because of its capacity to stay up with malware development. With the assistance of data science, security groups can detect malware with data driven tools and techniques. At last, data science empowered the malware detection to move from suspicion to realities. This paper depicts the utilization of data science approach for the malware detection.