Clustering Analysis of Email Malware Campaigns

The task of malware labeling on real datasets faces huge challenges—ever-changing datasets and lack of ground-truth labels—owing to the rapid growth of malware. Clustering malware on their respective families is a well known tool used for improving the efficiency of the malware labeling process. In this paper, we addressed the challenge of clustering email malware, and carried out a cluster analysis on a real dataset collected from email campaigns over a 13-month period. Our main original contribution is to analyze the usefulness of email’s header information for malware clustering (a novel approach proposed by Burton [1]), and compare it with features collected from the malware directly. We compare clustering based on email header’s information with traditional features extracted from varied resources provided by VirusTotal [2], including static and dynamic analysis. We show that email header information has an excellent performance.