Understanding the dynamic behavior of computer programs during normal working
conditions is an important task, which has multiple security benefits such as
the development of behavior-based anomaly detection, vulnerability discovery,
and patching. Existing works achieved this goal by collecting and analyzing
various data including network traffic, system calls, instruction traces, etc.
In this paper, we explore the use of a new type of data, performance counters,
to analyze the dynamic behavior of programs. Using existing primitives, we
develop a tool named perfextract to capture data from different performance
counters for a program during its startup time, thus forming multiple time
series to represent the dynamic behavior of the program. We analyze the
collected data and develop a semi-supervised clustering algorithm that allows
us to classify each program using its performance counter time series into a
specific group and to identify the intrinsic behavior of that group. We carry
out extensive experiments with 18 real-world programs that belong to 4 groups
including web browsers, text editors, image viewers, and audio players. The
experimental results show that the examined programs can be accurately
differentiated based on their performance counter data regardless of whether
programs are run in physical or virtual environments.

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