  A method for detecting electronic data theft from computer networks is
described, capable of recognizing patterns of remote exfiltration occurring
over days to weeks. Normal traffic flow data, in the form of a host's ingress
and egress bytes over time, is used to train an ensemble of one-class learners.
The detection ensemble is modular, with individual classifiers trained on
different traffic features thought to characterize malicious data transfers. We
select features that model the egress to ingress byte balance over time,
periodicity, short time-scale irregularity, and density of the traffic. The
features are most efficiently modeled in the frequency domain, which has the
added benefit that variable duration flows are transformed to a fixed-size
feature vector, and by sampling the frequency space appropriately,
long-duration flows can be tested. When trained on days- or weeks-worth of
traffic from individual hosts, our ensemble achieves a low false positive rate
(<2%) on a range of different system types. Simulated exfiltration samples with
a variety of different timing and data characteristics were generated and used
to test ensemble performance on different kinds of systems: when trained on a
client workstation's external traffic, the ensemble was generally successful at
detecting exfiltration that is not simultaneously ingress-heavy,
connection-sparse, and of short duration---a combination that is not optimal
for attackers seeking to transfer large amounts of data. Remote exfiltration is
more difficult to detect from egress-heavy systems, like web servers, with
normal traffic exhibiting timing characteristics similar to a wide range of
exfiltration types.
