Malware Detection and Prevention Using ML

Abstract

Abstract: The most prevalent issue on the internet today is malware. Due to its dynamic nature and ability to inherit characteristics from other types, polymorphic malware constantly modifies its properties to avoid being identified by traditional signature methodologies. The activity is carried out either at a certain moment or after a specific period of time. This study investigates machine learning model-based behavior-based detection techniques for detecting malware families and predict their presence through static or dynamic analysis.