Program Behavior Analysis and Clustering using Performance Counters

TL;DR

This paper introduces a novel approach using performance counters to analyze and cluster program behavior, enabling accurate classification of programs in various environments for security and analysis purposes.

Contribution

It develops perfextract for capturing performance counter data and a semi-supervised clustering algorithm for classifying program behaviors based on this data.

Findings

Programs can be accurately differentiated using performance counters.

Method works across physical and virtual environments.

Effective in classifying diverse real-world programs.

Abstract

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.