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Scalability, Fidelity and Stealth in the DRAKVUF Dynamic Malware Analysis System
In this paper we present DRAKVUF, a novel dynamic malware analysis system designed to address these challenges by building on the latest hardware virtualization extensions and the Xen hypervisor. We present a technique for improving stealth by initiating the execution of malware samples without leaving any trace in the analysis machine. We also present novel techniques to eliminate blind-spots created by kernel-mode rootkits by extending the scope of monitoring to include kernel internal functions, and to monitor file-system accesses through the kernel's heap allocations. With extensive tests performed on recent malware samples we show that DRAKVUF achieves significant improvements in conserving hardware resources while providing a stealthy, in-depth view into the behavior of modern malware.
University of Connecticut
University of Athens