In this paper, we present MAS, a practical memory analysis
\nsystem for identifying a kernel rootkit\u2019s memory
\nfootprint in an infected system. We also present two
\nlarge-scale studies of applying MAS to 848 real-world
\nWindows kernel crash dumps and 154,768 potential malware
\nsamples.
\nError propagation and invalid pointers are two key
\nchallenges that stop previous pointer-based memory
\ntraversal solutions from effectively and efficiently analyzing
\nreal-world systems. MAS uses a new memory
\ntraversal algorithm to support error correction and stop
\nerror propagation. Our enhanced static analysis allows
\nthe MAS memory traversal to avoid error-prone operations
\nand provides it with a reliable partial type assignment.
\nOur experiments show that MAS was able to analyze
\nall memory snapshots quickly with typical running times
\nbetween 30 and 160 seconds per snapshot and with near
\nperfect accuracy. Our kernel malware study observes
\nthat the malware samples we tested hooked 191 different
\nfunction pointers in 31 different data structures. With
\nMAS, we were able to determine quickly that 95 out of
\nthe 848 crash dumps contained kernel rootkits.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we present MAS, a practical memory analysis system for identifying a kernel rootkit\u2019s memory footprint in an infected system. We also present two large-scale studies of applying MAS to 848 real-world Windows kernel crash dumps and 154,768 potential malware samples. 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