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Kernel fuzzing is important for finding critical kernel vulnerabilities.Close-source (e.g., Windows) operating system kernel fuzzing is even morechallenging due to the lack of source code. Existing approaches fuzz the kernelby modeling syscall sequences from traces or static analysis of system codes.However, a common limitation is that they do not learn and mutate the syscallsequences to reach different kernel states, which can potentially result inmore bugs or crashes.  In this paper, we propose WinkFuzz, an approach to learn and mutate tracedsyscall sequences in order to reach different kernel states. WinkFuzz learnssyscall dependencies from the trace, identifies potential syscalls in the tracethat can have dependent subsequent syscalls, and applies the dependencies toinsert more syscalls while preserving the dependencies into the trace. ThenWinkFuzz fuzzes the synthesized new syscall sequence to find system crashes.  We applied WinkFuzz to four seed applications and found a total increase insyscall number of 70.8\%, with a success rate of 61\%, within three insertlevels. The average time for tracing, dependency analysis, recovering modelscript, and synthesizing script was 600, 39, 34, and 129 seconds respectively.The instant fuzzing rate is 3742 syscall executions per second. However, theaverage fuzz efficiency dropped to 155 syscall executions per second when theinitializing time, waiting time, and other factors were taken into account. Wefuzzed each seed application for 24 seconds and, on average, obtained 12.25crashes within that time frame.

model-based script synthesis for fuzzing