The quantification of normal and anomalous traffic flows
across computer networks is a topic of pervasive interest in network se-
curity, and requires the timely application of time-series methods. The
transmission or reception of packets passing between computers can be
represented in terms of time-stamped events and the resulting activity
understood in terms of point-processes. Interestingly, in the disparate do-
main of neuroscience, models for describing dependent point-processes
are well developed. In particular, spectral methods which decompose
second-order dependency across different frequencies allow for a rich
characterisation of point-processes. In this paper, we investigate using
the spectral coherence statistic to characterise computer network activ-
ity, and determine if, and how, device messaging may be dependent. We
demonstrate on real data, that for many devices there appears to be very
little dependency between device messaging channels. However, when sig-
nificant coherence is detected it appears highly structured, a result which
suggests coherence may prove useful for discriminating between types of
activity at the network level.