We present an epidemiological study of malware encounters in a large, multi-national enterprise. Our data sets allow us to observe or infer not only malware presence on enterprise computers, but also malware entry points, network locations of the computers (i.e., inside the enterprise network or outside) when the malware were encountered, and for some web-based malware encounters, web activities that gave rise to them. By coupling this data with demographic information for each host's primary user, such as his or her job title and level in the management hierarchy, we are able to paint a reasonably comprehensive picture of malware encounters for this enterprise. We use this analysis to build a logistic regression model for inferring the risk of hosts encountering malware; those ranked highly by our model have a > 3× higher rate of encountering malware than the base rate. We also discuss where our study confirms or refutes other studies and guidance that our results suggest.
Yen et al. REF use data about malware encounters in a large multi-national to predict the likelihood that hosts encounter malware based on the characteristics of these hosts' users.
An Epidemiological Study of Malware Encounters in a Large Enterprise
CCS '14
Computer Science