MLxPack: Investigating the Effects of Packers on ML-based Malware Detection Systems Using Static and Dynamic Traits

Malware is one of the serious computer security threats. To protect computers from infection, accurate detection of malware is essential. At the same time, malware detection faces two main practical challenges: the speed of malware development and their distribution continues to increase with complex methods to evade detection (such as a metamorphic or polymorphic malware). This research utilizes various characterizing features extracted from each malware using static and dynamic analysis to build seven machine learning models to detect and analyze packed windows malware. We use a large-scale dataset of over 107,000 samples covering unpacked and packed malware using ten different packers. We examined the performance of seven machine learning techniques using 50 dynamic and static features. Our results show that packed malware can circumvent detection when a single analysis is performed while applying both static and dynamic methods can help improve the detection accuracy around 2% to 3%.