Unsupervised Clustering for Identification of Malicious Domain Campaigns
Michael Weber, Jun Wang, Yuchen Zhou · 2018
New malicious domain campaigns often include large sets of domains registered in bulk and deployed simultaneously. Early identification of these campaigns can often be accomplished with distance functions or regular expressions of registered domains, but these methods may also miss some campaign domains. Other studies have used time-of-registration features to help identify malicious domains. This paper explores the use of unsupervised clustering based on passive DNS records and other inherent network information to identify domains that may be part of campaigns but resistant to detection by domain name or time-of-registration analysis alone. We have found that using this method, we can achieve up to 2.1x expansion from a seed of known campaign domains with less than 4% false positives. This could be a useful tool to augment other methods of identifying malicious domains.