Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 769624, 20 pages
http://dx.doi.org/10.1155/2015/769624
Research Article

Mal-Netminer: Malware Classification Approach Based on Social Network Analysis of System Call Graph

1Graduate School of Information Security, Korea University, Seoul 136-713, Republic of Korea
2Computer Science and Engineering Department, State University of New York at Buffalo (SUNY Buffalo), Buffalo, NY 14260-2500, USA

Received 19 May 2015; Accepted 3 August 2015

Academic Editor: Michael Small

Copyright © 2015 Jae-wook Jang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that “influence-based” graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%.