Richard Li, University of Utah; Min Du, University of California Berkeley; David Johnson, Robert Ricci, Jacobus Van der Merwe, and Eric Eide, University of Utah
Kernel-resident malware remains a significant threat. An effective way to detect such malware is to examine the kernel memory of many similar (virtual) machines, as one might find in an enterprise network or cloud, in search of anomalies: i.e., the relatively rare infected hosts within a large population of healthy hosts. It is challenging, however, to compare the kernel memories of different hosts against each other. Previous work has relied on knowledge of specific kernels—e.g., the locations of important variables and the layouts of key data structures—to cross the "semantic gap" and allow kernels to be compared. As a result, those previous systems work only with the kernels they were built for, and they make assumptions about the malware being searched for.
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