  In this paper, we present MORTON, a method that identifies compromised
devices in enterprise networks based on the existence of routine DNS
communication between devices and disreputable host names. With its compact
representation of the input data and use of efficient signal processing and a
neural network for classification, MORTON is designed to be accurate, robust,
and scalable. We evaluate MORTON using a large dataset of corporate DNS logs
and compare it with two recently proposed beaconing detection methods aimed at
detecting malware communication. The results demonstrate that while MORTON's
accuracy in a synthetic experiment is comparable to that of the other methods,
it outperforms those methods in terms of its ability to detect sophisticated
bot communication techniques, such as multistage channels, as well as in its
robustness and efficiency. In a real-world evaluation, which includes
previously unreported threats, MORTON and the two compared methods were
deployed to monitor the (unlabeled) DNS traffic of two global enterprises for a
week-long period; this evaluation demonstrates the effectiveness of MORTON in
real-world scenarios and showcases its superiority in terms of true and false
positive rates.
