Towards Architecture and OS-Independent Malware Detection via Memory Forensics

Abstract

In this work, we take a fundamentally different approach to the problem of analyzing a device for compromises via malware; our approach is OS and instruction architecture independent and relies only on having the raw binary data extracted from the memory dump of a device. Our system leverages a multi-hundred TB dataset of both compromised host memory dumps extracted from the MalRec dataset [8] and the first known dataset of benign host memory dumps running normal, non-compromised software. After an average of 30 to 45 seconds of pre-processing on a single memory dump, our system leverages both traditional machine learning and deep learning algorithms to achieve an average of 98% accuracy of detecting a compromised host.