Improving Memory Malware Detection in Machine Learning With Random Forest-Based Feature Selection
Qais Al-Na'amneh,
Ahmad Nawaf Nasayreh,
Rabia Al Mamlook
et al.
Abstract:Memory analysis is important in malware detection because it may capture a wide range of traits and behaviors. As aspects of technology evolve, so do the strategies used by malicious who aim to compromise the security and integrity of digital systems. This study investigates the classification of cyberattacks into malicious and benign. A specific malware memory dataset, MalMemAnalogy-2022, was created to test and evaluate this framework. In this chapter, a set of machine learning algorithms was used, including… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.