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

TL;DR

This paper introduces Mal-Netminer, a malware classification tool that leverages social network analysis of system call graphs to accurately identify malware families with over 96% accuracy.

Contribution

It applies social network analysis metrics to system call graphs for malware classification, demonstrating the effectiveness of influence-based features.

Findings

Degree centrality effectively classifies malware

Structural metrics are less effective for classification

Achieves over 96% accuracy in malware family detection

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%.