Skip to main content
Login | Suomeksi | På svenska | In English

A Feature-Based Call Graph Distance Measure for Program Similarity Analysis

Show full item record

Title: A Feature-Based Call Graph Distance Measure for Program Similarity Analysis
Author(s): Linkola, Simo
Contributor: University of Helsinki, Faculty of Science, Department of Computer Science
Discipline: Computer science
Language: English
Acceptance year: 2016
A measurement for how similar (or distant) two computer programs are has a wide range of possible applications. For example, they can be applied to malware analysis or analysis of university students' programming exercises. However, as programs may be arbitrarily structured, capturing the similarity of two non-trivial programs is a complex task. By extracting call graphs (graphs of caller-callee relationships of the program's functions, where nodes denote functions and directed edges denote function calls) from the programs, the similarity measurement can be changed into a graph problem. Previously, static call graph distance measures have been largely based on graph matching techniques, e.g. graph edit distance or maximum common subgraph, which are known to be costly. We propose a call graph distance measure based on features that preserve some structural information from the call graph without explicitly matching user defined functions together. We define basic properties of the features, several ways to compute the feature values, and give a basic algorithm for generating the features. We evaluate our features using two small datasets: a dataset of malware variants, and a dataset of university students' programming exercises, focusing especially on the former. For our evaluation we use experiments in information retrieval and clustering. We compare our results for both datasets to a baseline, and additionally for the malware dataset to the results obtained with a graph edit distance approximation. In our preliminary results we show that even though the feature generation approach is simpler than the graph edit distance approximation, the generated features can perform on a similar level as the graph edit distance approximation. However, experiments on larger datasets are still required to verify the results.

Files in this item

Files Size Format View
slinkola_thesis.pdf 9.908Mb PDF

This item appears in the following Collection(s)

Show full item record