Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection. (arXiv:2310.00516v2 [cs.CR] UPDATED)

Malware poses a significant security risk to individuals, organizations, and
critical infrastructure by compromising systems and data. Leveraging memory
dumps that offer snapshots of computer memory can aid the analysis and
detection of malicious content, including malware. To improve the efficacy and
address privacy concerns in malware classification systems, feature selection
can play a critical role as it is capable of identifying the most relevant
features, thus, minimizing the amount of data fed to classifiers. In this
study, we employ …

address aid analysis classification computer critical critical infrastructure data detection efficiency feature infrastructure malicious malware malware classification memory offer organizations privacy privacy concerns risk security security risk snapshots systems