  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 domain
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 activity, 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 significant 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.
