  We address the sequential change-point detection problem for the Gaussian
model where baseline distribution is Gaussian with variance \sigma^2 and mean
\mu such that \sigma^2=a\mu, where a>0 is a known constant; the change is in
\mu from one known value to another. First, we carry out a comparative
performance analysis of four detection procedures: the CUSUM procedure, the
Shiryaev-Roberts (SR) procedure, and two its modifications - the
Shiryaev-Roberts-Pollak and Shiryaev-Roberts-r procedures. The performance is
benchmarked via Pollak's maximal average delay to detection and Shiryaev's
stationary average delay to detection, each subject to a fixed average run
length to false alarm. The analysis shows that in practically interesting cases
the accuracy of asymptotic approximations is "reasonable" to "excellent". We
also consider an application of change-point detection to cybersecurity - for
rapid anomaly detection in computer networks. Using real network data we show
that statistically traffic's intensity can be well-described by the proposed
Gaussian model with \sigma^2=a\mu instead of the traditional Poisson model,
which requires \sigma^2=\mu. By successively devising the SR and CUSUM
procedures to "catch" a low-contrast network anomaly (caused by an ICMP
reflector attack), we then show that the SR rule is quicker. We conclude that
the SR procedure is a better cyber "watch dog" than the popular CUSUM
procedure.
