Fluorescence: Detecting Kernel-Resident Malware in Clouds


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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@inproceedings {242058,
author = {Richard Li and Min Du and David Johnson and Robert Ricci and Jacobus Van der Merwe and Eric Eide},
title = {Fluorescence: Detecting {Kernel-Resident} Malware in Clouds},
booktitle = {22nd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2019)},
year = {2019},
isbn = {978-1-939133-07-6},
address = {Chaoyang District, Beijing},
pages = {367--382},
url = {https://www.usenix.org/conference/raid2019/presentation/li-richard},
publisher = {USENIX Association},
month = sep,