As the number of cyber-attacks increases, there has
been increasing emphasis on developing complementary methods
of detection to the existing signature-based approaches. This work
builds upon a previously discovered persistent structure within
the Los Alamos National Laboratory network data sources,
to develop a regression based streaming anomaly detection
mechanism that can adapt to the network behaviour over time.
The methodology has also been applied to a new data set of the
same network to assess the extent of its pertinence in time.