Architecture for Resource-Aware VMI-based Cloud Malware Analysis

Abstract

Virtual machine introspection (VMI) is a technology with many possible applications, such as malware analysis and intrusion detection. However, this technique is resource intensive, as inspecting program behavior includes recording of a high number of events caused by the analyzed binary and related processes. In this paper we present an architecture that leverages cloud resources for virtual machine-based malware analysis in order to train a classifier for detecting cloud-specific malware. This architecture is designed while having in mind the resource consumption when applying the VMI-based technology in production systems, in particular the overhead of tracing a large set of system calls. In order to minimize the data acquisition overhead, we use a data-driven approach from the area of resource-aware machine learning. This approach enables us to optimize the trade-off between malware detection performance and the overhead of our VMI-based tracing system.